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This article proposes a One-Covariate-at-a-time Multiple Testing (OCMT) approach to choose significant variables in high-dimensional nonparametric additive regression models. Similarly to Chudik, Kapetanios, and Pesaran, we consider the statistical significance of individual nonparametric additive components one at a time and take into account the multiple testing nature of the problem. Both one-stage and multiple-stage procedures are considered. The former works well in terms of the true positive rate only if the net effects of all signals are strong enough; the latter helps to pick up hidden signals that have weak net effects. Simulations demonstrate the good finite-sample performance of the proposed procedures. As an empirical illustration, we apply the OCMT procedure to a dataset extracted from the Longitudinal Survey on Rural Urban Migration in China. We find that our procedure works well in terms of out-of-sample root mean square forecast errors, compared with competing methods such as adaptive group Lasso (AGLASSO). [ABSTRACT FROM AUTHOR]

Benchmark dose analysis aims to estimate the level of exposure to a toxin associated with a clinically significant adverse outcome and quantifies uncertainty using the lower limit of a confidence interval for this level. We develop a novel framework for benchmark dose analysis based on monotone additive dose-response models. We first introduce a flexible approach for fitting monotone additive models via penalized B-splines and Laplace-approximate marginal likelihood. A reflective Newton method is then developed that employs de Boor's algorithm for computing splines and their derivatives for efficient estimation of the benchmark dose. Finally, we develop a novel approach for calculating benchmark dose lower limits based on an approximate pivot for the nonlinear equation solved by the estimated benchmark dose. The favorable properties of this approach compared to the Delta method and a parameteric bootstrap are discussed. We apply the new methods to make inferences about the level of prenatal alcohol exposure associated with clinically significant cognitive defects in children using data from six NIH-funded longitudinal cohort studies. Software to reproduce the results in this paper is available online and makes use of the novel semibmd  R package, which implements the methods in this paper. [ABSTRACT FROM AUTHOR]

Abstract– Distributed statistical learning has become a popular technique for large-scale data analysis. Most existing work in this area focuses on dividing the observations, but we propose a new algorithm, DDAC-SpAM, which divides the features under a high-dimensional sparse additive model. Our approach involves three steps: divide, decorrelate, and conquer. The decorrelation operation enables each local estimator to recover the sparsity pattern for each additive component without imposing strict constraints on the correlation structure among variables. The effectiveness and efficiency of the proposed algorithm are demonstrated through theoretical analysis and empirical results on both synthetic and real data. The theoretical results include both the consistent sparsity pattern recovery as well as statistical inference for each additive functional component. Our approach provides a practical solution for fitting sparse additive models, with promising applications in a wide range of domains. for this article are available online. [ABSTRACT FROM AUTHOR]

Purpose: Tobacco smoking and alcohol drinking are positively associated with psoriasis prevalence and disease severity. Researches focusing on the influence of smoking and drinking on the treatment efficacy of psoriasis are still limited, especially their interaction effect. This study aims to explore the interactive effects of smoking and drinking on the treatment efficacy in psoriasis patients. Patients and Methods: From 2021 to 2022, we recruited 560 patients with psoriasis from Shanghai Skin Diseases Hospital. Demographic and clinical features as well as treatment efficacy were collected through questionnaire interview and physical examination during patient's hospital visit at week 0, week 4 and week 8. Logistic regression model was used to explore the influence of smoking and drinking on the treatment efficacy in psoriasis patients, and multiplicative and additive interaction models were used to verify the interaction effect of smoking and drinking on the treatment efficacy. Results: The prevalence of smoking and drinking among psoriasis patients was respectively 43.8% and 25.4%, and 19.6% of them with both smoking and drinking. Logistic regression analysis showed that patients with smoking (OR=7.78, 95% CI: 5.26~11.49) and drinking (OR=5.21, 95% CI: 3.29~8.27) had higher risk of experiencing the failure to achieve PASI75 at week 8, even with the adjustment of confounders. Moreover, multiplicative as well as additive model showed that tobacco smoking interacted with alcohol drinking which influenced the treatment efficacy more severely (OR=12.74, 95% CI: 7.16~22.67). The proportion of PASI75 achievement in female patients (OR=19.54) and patients with methotrexate (OR=28.31) and biologics (OR=21.61) were more likely being affected by smoking and drinking. Conclusion: Tobacco smoking and alcohol drinking could increase the failure of PASI75 achievement in patients with psoriasis, individually and interactively. We recommend that dermatologists should educate patients to pay attention to the negative effects of smoking and drinking, encourage them to quit, and thus improve the treatment efficacy. [ABSTRACT FROM AUTHOR]

Analysis of the restricted mean survival time (RMST) has become increasingly common in biomedical studies during the last decade as a means of estimating treatment or covariate effects on survival. Advantages of RMST over the hazard ratio (HR) include increased interpretability and lack of reliance on the often tenuous proportional hazards assumption. Some authors have argued that RMST regression should generally be the frontline analysis as opposed to methods based on counting process increments. However, in order for the use of the RMST to be more mainstream, it is necessary to broaden the range of data structures to which pertinent methods can be applied. In this report, we address this issue from two angles. First, most of existing methodological development for directly modeling RMST has focused on multiplicative models. An additive model may be preferred due to goodness of fit and/or parameter interpretation. Second, many settings encountered nowadays feature high‐dimensional categorical (nuisance) covariates, for which parameter estimation is best avoided. Motivated by these considerations, we propose stratified additive models for direct RMST analysis. The proposed methods feature additive covariate effects. Moreover, nuisance factors can be factored out of the estimation, akin to stratification in Cox regression, such that focus can be appropriately awarded to the parameters of chief interest. Large‐sample properties of the proposed estimators are derived, and a simulation study is performed to assess finite‐sample performance. In addition, we provide techniques for evaluating a fitted model with respect to risk discrimination and predictive accuracy. The proposed methods are then applied to liver transplant data to estimate the effects of donor characteristics on posttransplant survival time. [ABSTRACT FROM AUTHOR]

Fanlingzi Shen,1,2,* Yu Song,3,* Yan Qiang,1,* Xiangjin Gao,1 Siyuan Li,2 Rui Zhang,1 Zhongzhi Gao,1 Bin Li,1 Wencheng Jiang,4 Ruiping Wang1,2 1Clinical Research Center, Shanghai Skin Diseases Hospital, School of Medicine, Tongji University, Shanghai, People’s Republic of China; 2School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, People’s Republic of China; 3Department of Dermatology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, People’s Republic of China; 4Department of Traditional Chinese Medicine, Shanghai Skin Diseases Hospital, School of Medicine, Tongji University, Shanghai, People’s Republic of China*These authors contributed equally to this workCorrespondence: Wencheng Jiang, Department of Traditional Chinese Medicine, Shanghai Skin Diseases Hospital, Medical School, Tongji University, 1278 Baode Road, Shanghai, 200443, People’s Republic of China, Email drjiangwencheng@163.com Ruiping Wang, Clinical Research & Innovation Transformation Center, Shanghai Skin Diseases Hospital, Medical School, Tongji University, 1278 Baode Road, Shanghai, 200443, People’s Republic of China, Email w19830901@126.comPurpose: Tobacco smoking and alcohol drinking are positively associated with psoriasis prevalence and disease severity. Researches focusing on the influence of smoking and drinking on the treatment efficacy of psoriasis are still limited, especially their interaction effect. This study aims to explore the interactive effects of smoking and drinking on the treatment efficacy in psoriasis patients.Patients and Methods: From 2021 to 2022, we recruited 560 patients with psoriasis from Shanghai Skin Diseases Hospital. Demographic and clinical features as well as treatment efficacy were collected through questionnaire interview and physical examination during patient’s hospital visit at week 0, week 4 and week 8. Logistic regression model was used to explore the influence of smoking and drinking on the treatment efficacy in psoriasis patients, and multiplicative and additive interaction models were used to verify the interaction effect of smoking and drinking on the treatment efficacy.Results: The prevalence of smoking and drinking among psoriasis patients was respectively 43.8% and 25.4%, and 19.6% of them with both smoking and drinking. Logistic regression analysis showed that patients with smoking (OR=7.78, 95% CI: 5.26~11.49) and drinking (OR=5.21, 95% CI: 3.29~8.27) had higher risk of experiencing the failure to achieve PASI75 at week 8, even with the adjustment of confounders. Moreover, multiplicative as well as additive model showed that tobacco smoking interacted with alcohol drinking which influenced the treatment efficacy more severely (OR=12.74, 95% CI: 7.16~22.67). The proportion of PASI75 achievement in female patients (OR=19.54) and patients with methotrexate (OR=28.31) and biologics (OR=21.61) were more likely being affected by smoking and drinking.Conclusion: Tobacco smoking and alcohol drinking could increase the failure of PASI75 achievement in patients with psoriasis, individually and interactively. We recommend that dermatologists should educate patients to pay attention to the negative effects of smoking and drinking, encourage them to quit, and thus improve the treatment efficacy.Keywords: psoriasis, tobacco smoking, alcohol drinking, interaction, additive model, multiplicative model

The focus of this article is on studying a partially linear additive model, which is defined using a measurable function ψ : R q → R . The model is given as follows: ψ (Y i) := Y i = Z i ⊤ β + ∑ ℓ = 1 d m ℓ (X ℓ , i) + ε i for 1 ≤ i ≤ n , where Z i = (Z i , 1 , ... , Z i p ) ⊤ and X i = (X 1 , i , ... , X i d ) ⊤ are vectors of explanatory variables, β = (β 1 , ... , β p ) ⊤ is a vector of unknown parameters, m 1 , ... , m d are unknown univariate real functions, and ε 1 , ... , ε n are independent random errors with mean zero, finite variances σε. Additionally, it is assumed that E (ε | X , Z) = 0 almost surely. The main contributions of this article are as follows. First, under certain mild conditions, we establish the asymptotic normality of the non linear additive components of the model. These components are estimated using the marginal integration device with the linear wavelet method. Second, we leverage our main result to construct confidence intervals for the estimated model. [ABSTRACT FROM AUTHOR]

In this paper Chen-Burr XII distribution is constructed and graphical description of the probability density function, hazard rate and reversed hazard rate functions of the proposed model is obtained. Also, some statistical characteristics of the Chen-Burr XII distribution are discussed and some new models as sub-models from the Chen-Burr XII distribution are introduced. Moreover, maximum likelihood estimation of the parameters, reliability, hazard rate and reversed hazard rate functions of the Chen-Burr XII distribution are considered. Also, the asymptotic confidence intervals of the distribution parameters, reliability, hazard rate and reversed hazard rate functions are presented. Finally, three real life data sets are applied to prove how the Chen-Burr XII distribution can be applied in real life and to confirm its superiority over some existing distributions. [ABSTRACT FROM AUTHOR]

Quantile regression has emerged as one of the standard tools for regression analysis that enables a proper assessment of the complete conditional distribution of responses. This article considers a valuable alternative class to quantiles, called extremiles. Extremiles bear much better than quantiles the burden of representing an alert risk measure to the magnitude of infrequent catastrophic losses. Additive regression model has concentrated on making the regression structure more flexible by including nonlinear effects of continuous covariates and interaction effects. As a consequence, additive extremile regression based on minimizing an asymmetrically weighted sum of squared residuals is introduced. Different estimation procedures are presented including iteratively penalized least asymmetric weighted squares and gradient descent boosting. The properties of these procedures are investigated in a simulation study and an analysis of tropical cyclone intensity of the North Atlantic which reveals the function of variable selection by modelling additive extremile regression simultaneously. [ABSTRACT FROM AUTHOR]

This paper introduces a new four-parameter additive model, named xgamma-Burr XII distribution, by considering two competing risks: the former has the xgamma distribution and the latter has the Burr XII distribution. A graphical description of the xgamma-Burr XII distribution is presented, including plots of the probability density function, hazard rate and reversed hazard rate functions. The xgamma-Burr XII density has different shapes such as decreasing, unimodal, approximately symmetric and decreasing-unimodal. The main statistical properties of the proposed model are studied. The unknown model parameters, reliability, hazard rate and reversed hazard rate functions are estimated via the maximum likelihood method. The asymptotic confidence intervals of the parameters, reliability function, hazard rate function and reversed hazard rate function are also obtained. A simulation study is carried out to evaluate the performance of the maximum likelihood estimates. In addition, three real data are applied to show the superiority of the xgamma-Burr XII distribution over some known distributions in real-life applications. [ABSTRACT FROM AUTHOR]

The traditional volume-derived biomass method is limited because it does not fully consider the carbon sink of young trees, which leads to the underestimation of the carbon sink capacity of a forest ecosystem. Therefore, there is an urgent need to establish an allometric biomass model of young trees to provide a quantitative basis for accurately estimating the carbon storage and carbon sink of young trees. The destructive data that were used in this study included the biomass of the young trees of the two dominant species (Betula pendula subsp. mandshurica (Regel) Ashburner & McAll and Populus × tomentosa Carrière) in China, which was composed of the aboveground biomass (Ba), belowground biomass (Bb), and total biomass (Bt). Univariate and bivariate dimensions were selected and five candidate biomass models were independently tested. Two additive allometric biomass model systems of young trees were established using the proportional function control method and algebraic sum control method, respectively. We found that the logistic function was the most suitable for explaining the allometric growth relationship between the Ba, Bt, and diameter at breast height (D) of young trees; the power function was the most suitable for explaining the allometric growth relationship between the Bb and D of young trees. When compared with the independent fitting model, the two additive allometric biomass model systems provide additive biomass prediction which reflects the conditions in reality. The accuracy of the Bt models and Ba models was higher, while the accuracy of the Bb models was lower. In terms of the two dimensions—univariate and bivariate, we found that the bivariate additive allometric biomass model system was more accurate. In the univariate dimension, the proportional function control method was superior to the algebraic sum control method. In the bivariate dimension, the algebraic sum control method was superior to the proportional function control method. The additive allometric biomass models provide a reliable basis for estimating the biomass of young trees and realizing the additivity of the biomass components, which has broad application prospects, such as the monitoring of carbon stocks and carbon sink evaluation. [ABSTRACT FROM AUTHOR]

As a result of intensive herbicide protection of crops against silky bentgrass (Apera spica-venti (L.) P. Beauv), numerous herbicide-resistant biotypes have been selected, mainly from the group of acetolactate synthase (ALS) inhibitors. We analyzed the development of herbicide-sensitive and herbicide-resistant biotypes of bentgrass and winter wheat under competition, taking into account selected physical and chemical properties of the soil, including nitrogen fertilization. The pot experiment (additive model) was conducted in the 2018/19 and 2019/20 seasons. The experimental factors included: (1) bentgrass with different sensitivity levels to herbicides from the groups HRAC/WSSA 1 and HRAC/WSSA 2, specifically two sensitive and three resistant biotypes; (2) two types of soil, sandy and clay; and (3) nitrogen fertilization, both with and without fertilization. Winter wheat and bentgrass development was assessed during each growing season, every 5 or 3 days from September until May, using the BBCH scale. The emergence date of the tested species/biotypes was recorded. The development of competing species was compared based on a new, proposed index: the duration of the developmental phases. As a result, the soil type and fertilization level differentiated wheat and bentgrass emergence dates and development. The autumn development of the competing species was slower and more uniform than the spring one. At the same time, the dynamics of the bentgrass and wheat development in the spring were greater. Bentgrass and winter wheat emerged earlier and grew more intensively on clay and fertilized soil. To sum up, no clear relationship was found between the resistance/sensitivity of bentgrass to herbicides and changes in the phenological development of plants in competition with winter wheat. [ABSTRACT FROM AUTHOR]

We are mainly concerned with the partially linear additive model defined for a measurable function ψ : R q → R , by ψ (Y i) : = Y i = Z i ⊤ β + ∑ l = 1 d m l (X l , i) + ε i for 1 ≤ i ≤ n , where Z i = (Z 1 , i , ... , Z p , i) ⊤ and X i = (X 1 , i , ... , X i , d) ⊤ are vectors of explanatory variables, β = (β 1 , ... , β p) ⊤ is a vector of unknown parameters, m 1 , ... , m d are unknown univariate real functions, and ε 1 , ... , ε n are independent random errors with mean zero, finite variances σ ε and E (ε | X , Z) = 0 a.s. Under some mild conditions, we present a sharp uniform-in-bandwidth limit law for the nonlinear additive components of the model estimated by the marginal integration device with the kernel method. We allow the bandwidth to varying within the complete range for which the estimator is consistent. We provide the almost sure simultaneous asymptotic confidence bands for the regression functions. [ABSTRACT FROM AUTHOR]

Heart rate (HR) and blood pressure (BP) measured by a patient can be used to monitor response to pharmacologic therapies. Continuously measured HR and BP are multivariate time series as sequences of values at regularly spaced intervals over time and changes in pharmacologic therapies interrupt the multivariate time series and create phases. In the multivariate interrupted time series, there are change processes in multiple phases, such as level changes, linear or non-linear trend changes, and time-varying serial dependence. This paper presents an application of a multivariate dynamic additive model to account for these change processes. In addition, a simulation study is conducted to evaluate the model's parameter recovery and to demonstrate the consequences of ignoring the time-varying serial dependence in HR and BP when detecting level changes and trend changes. The results of the simulation study show that the accuracy and precision of parameter estimates are satisfactory. Furthermore, the simulation results present that ignoring time-varying serial dependence in the same conditions as those found in the application results in biased estimates and standard errors for the level changes and trend changes. [ABSTRACT FROM AUTHOR]

The article deals with the development and implementation of a financial model of exports in the context of strategic planning of foreign economic activity of enterprises. The model described in this paper aims to measure, analyse, evaluate and propose improvements to administrative processes by identifying various variables involved in strategic foreign trade planning. This paper is a survey that is being developed for export activities in Ukraine. Exports in Ukraine were analysed to test the methodology and to build an additive model. The model empirically shows the links that exist between the internal functions of a company and its ability to generate performance management indicators and business dynamics as a way to improve processes in strategic foreign trade planning. Some relevant findings from the first two stages of the empirical research conducted to date are presented. Validation has proven that the use of the methodology of strategic planning of foreign economic activity allows you to compensate for investments in new resources by increasing the number of operations carried out eliminating delays and attracting new customers as a result of improving the client's perception of the quality of service. During the audit, it was possible to determine that the delays created in the activities of support systems increase their negative impact on the process, consuming the slack of many actions of the central system and delaying their implementation, negatively affecting foreign economic activity. The model will be useful for enterprises engaged in foreign trade. [ABSTRACT FROM AUTHOR]

Research background: The effectiveness of public transport is currently a very significant topic, which is also influencing the total efficiency of the public budget (state or regional). Public transport companies (bus, rail) are usually providing their services under the public contract with the state or regional authority responsible for the ordering of these services, and they are receiving subsidies form the public budget. It is in the interest of all inhabitants that these funds will be used properly, and they will receive the corresponding quality of public transport services. Purpose of the article: The main aim of the article is to evaluate the productive effectiveness of chosen public transport companies operating in the suburban bus transport in Slovakia and Czech Republic. Methods: For evaluating the effectiveness of specific groups of specific groups oi public transport companies, an additive (weighted) approach of Data envelopment analysis (DEA), so-called slack-based model is used. Based on the results from SBM model, individual hypotheses about the significance of efficiency rate are tested using non-parametric Wilcoxon tests for one sample and Mann-Whitney test for independent samples. Findings & Value added: From the results of the efficiency evaluation using SBM DEA model it is obvious that the average productive effectiveness of public transport companies operating under PSO contracts is in general low, with two or three DMU's achieving maximum efficiency within the given group. The significance of the difference in efficiency rate between two compared countries is also negligible. The main advantage of using the additive approach for this evaluation consists in considering the weights of inputs and outputs in the model. Suggested methodology can also be used by public authorities responsible for ordering transport services which are supported from the public budget. [ABSTRACT FROM AUTHOR]

The growing complexity of contemporary software systems, with multiple sub-modules, demands innovative modeling approaches, especially in addressing intricate failures and masked data generation. This paper proposed a stochastic differential equation (SDE)-based software reliability additive model tailored for multi-component systems dealing with masked data. However, in the presence of masked data, the challenge of parameter estimation arises due to the inherent complexity of the objective function and numerous parameters. To overcome this, we proposed the Expectation Least Square (ELS) algorithm, providing a strategic solution for the intricate task of parameter estimation with masked data. In order to verify the effectiveness of our proposed model, we conducted a comparative analysis with traditional reliability models using three actual failure data sets. The experimental results emphasize the superior performance of the proposed model and highlight the effectiveness of the ELS algorithm in accurately estimating software reliability.

The purpose of the study is to classify and evaluate indicators of social enterprise development for countries with the most favourable conditions for their functioning. Methodology. The study uses indices as an assessment tool. The method of grouping indicators was used, which allowed to identify two components of social entrepreneurship development: economic and social. The basis of the analysis is the use of additive, multiplicative and additive-multiplicative models, which allows comparing the results and determining the most effective model for a particular country. To evaluate the development of social entrepreneurship, the Thomson Reuters Foundation report "The best countries to be a social entrepreneur" was used. Results. Studies have shown that the highest value of the social enterprise development index is achieved when using different models depending on the country chosen, i.e., if the highest level is achieved when using an additive model (Singapore, Denmark, Chile), this means that the low level of development of one component is compensated for by a higher level of other components. If the highest value is achieved when using a multiplier model (Canada, Australia, France, Belgium, the Netherlands, Finland, Indonesia), then it is important for the country to take into account all development components simultaneously. The additive-multiplicative model allows countries to vary the components and determine how they want to move forward to achieve the highest level of social entrepreneurship development. Practical implications. The classification and evaluation of indicators for countries allows to identify "stimulators" and "disincentives" for the development of a social enterprise, as well as to determine the nature of their impact: economic (through material incentives), non-economic (social). This allows each country to develop its own algorithm for implementing such an innovative form of business to achieve maximum effect, i.e., to solve socio-economic problems and increase the level of development in the future. Value/originality. In the context of escalating conflicts at both the global and local levels, the number and complexity of socio-economic problems are increasing, and they need to be addressed through the use of creative and innovative methods, as traditional mechanisms have failed. That is why social enterprises are an effective form of business that will allow not only quantitatively but also qualitatively to ensure the achievement of this mission. This research focuses on the factors that influence the development of social enterprises and can be used by countries to formulate public policies to support this innovative form of business.

Liebig's law of the minimum (LLM) is often used to interpret empirical biological growth data and model multiple substrates co-limited growth. However, its mechanistic foundation is rarely discussed, even though its validity has been questioned since its introduction in the 1820s. Here we first show that LLM is a crude approximation of the law of mass action, the state of art theory of biochemical reactions, and the LLM model is less accurate than two other approximations of the law of mass action: the synthesizing unit model and the additive model. We corroborate this conclusion using empirical data sets of algae and plants grown under two co-limiting substrates. Based on our analysis, we show that when growth is modeled directly as a function of substrate uptake, the LLM model improperly restricts the organism to be of fixed elemental stoichiometry, making it incapable of consistently resolving biological adaptation, ecological evolution, and community assembly. When growth is modeled as a function of the cellular nutrient quota, the LLM model may obtain good results at the risk of incorrect model parameters as compared to those inferred from the more accurate synthesizing unit model. However, biogeochemical models that implement these three formulations are needed to evaluate which formulation is acceptably accurate and their impacts on predicted long-term ecosystem dynamics. In particular, studies are needed that explore the extent to which parameter calibration can rescue model performance when the mechanistic representation of a biogeochemical process is known to be deficient.

In this paper Chen-Burr XII distribution is constructed and graphical description of the probability density function, hazard rate and reversed hazard rate functions of the proposed model is obtained. Also, some statistical characteristics of the Chen-Burr XII distribution are discussed and some new models as sub-models from the Chen-Burr XII distribution are introduced. Moreover, maximum likelihood estimation of the parameters, reliability, hazard rate and reversed hazard rate functions of the Chen-Burr XII distribution are considered. Also, the asymptotic confidence intervals of the distribution parameters, reliability, hazard rate and reversed hazard rate functions are presented. Finally, three real life data sets are applied to prove how the Chen-Burr XII distribution can be applied in real life and to confirm its superiority over some existing distributions.

As a result of intensive herbicide protection of crops against silky bentgrass (Apera spica-venti (L.) P. Beauv), numerous herbicide-resistant biotypes have been selected, mainly from the group of acetolactate synthase (ALS) inhibitors. We analyzed the development of herbicide-sensitive and herbicide-resistant biotypes of bentgrass and winter wheat under competition, taking into account selected physical and chemical properties of the soil, including nitrogen fertilization. The pot experiment (additive model) was conducted in the 2018/19 and 2019/20 seasons. The experimental factors included: (1) bentgrass with different sensitivity levels to herbicides from the groups HRAC/WSSA 1 and HRAC/WSSA 2, specifically two sensitive and three resistant biotypes; (2) two types of soil, sandy and clay; and (3) nitrogen fertilization, both with and without fertilization. Winter wheat and bentgrass development was assessed during each growing season, every 5 or 3 days from September until May, using the BBCH scale. The emergence date of the tested species/biotypes was recorded. The development of competing species was compared based on a new, proposed index: the duration of the developmental phases. As a result, the soil type and fertilization level differentiated wheat and bentgrass emergence dates and development. The autumn development of the competing species was slower and more uniform than the spring one. At the same time, the dynamics of the bentgrass and wheat development in the spring were greater. Bentgrass and winter wheat emerged earlier and grew more intensively on clay and fertilized soil. To sum up, no clear relationship was found between the resistance/sensitivity of bentgrass to herbicides and changes in the phenological development of plants in competition with winter wheat.

The traditional volume-derived biomass method is limited because it does not fully consider the carbon sink of young trees, which leads to the underestimation of the carbon sink capacity of a forest ecosystem. Therefore, there is an urgent need to establish an allometric biomass model of young trees to provide a quantitative basis for accurately estimating the carbon storage and carbon sink of young trees. The destructive data that were used in this study included the biomass of the young trees of the two dominant species (Betula pendula subsp. mandshurica (Regel) Ashburner & McAll and Populus × tomentosa Carrière) in China, which was composed of the aboveground biomass (Ba), belowground biomass (Bb), and total biomass (Bt). Univariate and bivariate dimensions were selected and five candidate biomass models were independently tested. Two additive allometric biomass model systems of young trees were established using the proportional function control method and algebraic sum control method, respectively. We found that the logistic function was the most suitable for explaining the allometric growth relationship between the Ba, Bt, and diameter at breast height (D) of young trees; the power function was the most suitable for explaining the allometric growth relationship between the Bb and D of young trees. When compared with the independent fitting model, the two additive allometric biomass model systems provide additive biomass prediction which reflects the conditions in reality. The accuracy of the Bt models and Ba models was higher, while the accuracy of the Bb models was lower. In terms of the two dimensions—univariate and bivariate, we found that the bivariate additive allometric biomass model system was more accurate. In the univariate dimension, the proportional function control method was superior to the algebraic sum control method. In the bivariate dimension, the algebraic sum control method was superior to the proportional function control method. The additive allometric biomass models provide a reliable basis for estimating the biomass of young trees and realizing the additivity of the biomass components, which has broad application prospects, such as the monitoring of carbon stocks and carbon sink evaluation.

The article deals with the development and implementation of a financial model of exports in the context of strategic planning of foreign economic activity of enterprises. The model described in this paper aims to measure, analyse, evaluate and propose improvements to administrative processes by identifying various variables involved in strategic foreign trade planning. This paper is a survey that is being developed for export activities in Ukraine. Exports in Ukraine were analysed to test the methodology and to build an additive model. The model empirically shows the links that exist between the internal functions of a company and its ability to generate performance management indicators and business dynamics as a way to improve processes in strategic foreign trade planning. Some relevant findings from the first two stages of the empirical research conducted to date are presented. Validation has proven that the use of the methodology of strategic planning of foreign economic activity allows you to compensate for investments in new resources by increasing the number of operations carried out eliminating delays and attracting new customers as a result of improving the client's perception of the quality of service. During the audit, it was possible to determine that the delays created in the activities of support systems increase their negative impact on the process, consuming the slack of many actions of the central system and delaying their implementation, negatively affecting foreign economic activity. The model will be useful for enterprises engaged in foreign trade.

Tensor regression methods have been widely used to predict a scalar response from covariates in the form of a multiway array. In many applications, the regions of tensor covariates used for prediction are often spatially connected with unknown shapes and discontinuous jumps on the boundaries. Moreover, the relationship between the response and the tensor covariates can be nonlinear. In this article, we develop a nonlinear Bayesian tensor additive regression model to accommodate such spatial structure. A functional fused elastic net prior is proposed over the additive component functions to comprehensively model the nonlinearity and spatial smoothness, detect the discontinuous jumps, and simultaneously identify the active regions. The great flexibility and interpretability of the proposed method against the alternatives are demonstrated by a simulation study and an analysis on facial feature data. [ABSTRACT FROM AUTHOR]

Laplace P-splines (LPS) combine the P-splines smoother and the Laplace approximation in a unifying framework for fast and flexible inference under the Bayesian paradigm. The Gaussian Markov random field prior assumed for penalized parameters and the Bernstein-von Mises theorem typically ensure a razor-sharp accuracy of the Laplace approximation to the posterior distribution of these quantities. This accuracy can be seriously compromised for some unpenalized parameters, especially when the information synthesized by the prior and the likelihood is sparse. Therefore, we propose a refined version of the LPS methodology by splitting the parameter space in two subsets. The first set involves parameters for which the joint posterior distribution is approached from a non-Gaussian perspective with an approximation scheme tailored to capture asymmetric patterns, while the posterior distribution for the penalized parameters in the complementary set undergoes the LPS treatment with Laplace approximations. As such, the dichotomization of the parameter space provides the necessary structure for a separate treatment of model parameters, yielding improved estimation accuracy as compared to a setting where posterior quantities are uniformly handled with Laplace. In addition, the proposed enriched version of LPS remains entirely sampling-free, so that it operates at a computing speed that is far from reach to any existing Markov chain Monte Carlo approach. The methodology is illustrated on the additive proportional odds model with an application on ordinal survey data. [ABSTRACT FROM AUTHOR]

This paper proposed a general framework based on semiparametric additive mixed effects model to identify subgroups on each covariate and estimate the corresponding regression functions simultaneously for longitudinal data, thus it could reveal which covariate contributes to the existence of subgroups among population. A backfitting combined with k-means algorithm was developed to detect subgroup structure on each covariate and estimate each semiparametric additive component across subgroups. A Bayesian information criterion is employed to estimate the actual number of groups. The efficacy and accuracy of the proposed procedure in identifying the subgroups and estimating the regression functions are illustrated through numerical studies. In addition, the authors demonstrate the usefulness of the proposed method with applications to PBC data and Industrial Portfolio's Return data and provide meaningful partitions of the populations. [ABSTRACT FROM AUTHOR]

The randomized controlled trial (RCT) research design assumes that a drug's "specific" effect can be isolated, added, and subtracted from the "nonspecific" effect of context and person. While RCTs are helpful in assessing the added benefit of a novel drug, they tend to obscure the curative potential of extra-pharmacological variables, known as "the placebo effect." Ample empirical evidence suggests that person/context-dependent physical, social, and cultural variables not only add to, but also shape drug effects, making them worth harnessing for patient benefits. Nevertheless, utilizing placebo effects in medicine is challenging due to conceptual and normative obstacles. In this article, we propose a new framework inspired by the field of psychedelic science and its employment of the "set and setting" concept. This framework acknowledges that drug and nondrug factors have an interactive and synergistic relationship. From it, we suggest ways to reintegrate nondrug variables into the biomedical toolbox, to ethically harness the placebo effect for improved clinical care. [ABSTRACT FROM AUTHOR]

Additive model plays an important role in machine learning due to its flexibility and interpretability in the prediction function. However, solving large-scale additive models is a challenging task due to several difficulties. Until now, scaling up additive models is still an open problem. To address this challenging problem, in this paper, we propose a new doubly stochastic optimization algorithm for solving the generalized additive models (DSGAM). We first propose a generalized formulation of additive models without the orthogonal hypothesis on the basis function. After that, we propose a wrapper algorithm to optimize the generalized additive models. Importantly, we introduce a doubly stochastic gradient algorithm (DSG) to solve an inner subproblem in the wrapper algorithm, which can scale well in sample size and dimensionality simultaneously. Finally, we prove the fast convergence rate of our DSGAM algorithm. The experimental results on various large-scale benchmark datasets not only confirm the fast convergence of our DSGAM algorithm, but also show a huge reduction of computational time compared with existing algorithms, while retaining the similar generalization performance. [ABSTRACT FROM AUTHOR]

Nonparametric regression estimation with Gaussian measurement errors in predictors is a classical statistical problem. It is well known that the errors dramatically slow down the rate of regression estimation, and this paper complement that result by presenting a sharp constant. Then an interesting example of using this sharp constant to discover a new curse of dimensionality in functional nonparametric regression is presented, and analysis of real data complements the theory. [ABSTRACT FROM AUTHOR]

The efficiency of agricultural enterprises is determined by the synergistic influence of an entire set of various factors. Improvement and testing of methodological support for factor-complementary diagnostics of the level of economic efficiency of business entities in modern conditions is still an urgent issue. The purpose of this study was to comprehensively monitor the level of economic efficiency of agricultural enterprises in Ukraine and measure the degree of influence of priority factors on the aggregate efficiency indicator. The study involved several methods: abstract-logical, comparative analysis, tabular, statistical, matriX-coordinate method, calculation-constructive, monographic, factor analysis. It was established that during 2015 and 2021, this aggregate indicator increased by 47% – from 15.07 to 22.13 points. Among the factors that had a positive effect on the established growth of the overall indicator of economic efficiency, the largest rating coefficients have: an increase in labour productivity (1.5852), a more than two-fold increase in the amount of net profit of agricultural enterprises (1.0931), a 2.2-fold increase in the coefficient financing (0.905), a 1.9-fold increase in the loan capital turnover ratio (0.8754) and an almost 3-fold increase in its profitability (0.8587), an increase in the receivables turnover indeX (0.7899) and a two-fold increase in the absolute liquidity indeX (0.7500). The following influencing factors had the most negative effect on the level of the overall efficiency indicator: a 23% decrease in the fund return indicator (-0.6005), a 17% decrease in the quick liquidity ratio (-0.4944), a 26% deterioration in the profitability of fiXed assets (-0. 4642), a decrease in turnover and profitability of the equity capital of agricultural enterprises (-0.4653 and -0.4426, respectively). The practical significance of this study is determined by the improvement of methodological tools for measuring and diagnosing the aggregate level of economic efficiency of business entities, which can be useful in the management practice of organizing monitoring and developing areas for improving the economic efficiency of agricultural enterprises

This paper introduces a new four-parameter additive model, named xgamma-Burr XII distribution, by considering two competing risks: the former has the xgamma distribution and the latter has the Burr XII distribution. A graphical description of the xgamma-Burr XII distribution is presented, including plots of the probability density function, hazard rate and reversed hazard rate functions. The xgamma-Burr XII density has different shapes such as decreasing, unimodal, approximately symmetric and decreasing-unimodal. The main statistical properties of the proposed model are studied. The unknown model parameters, reliability, hazard rate and reversed hazard rate functions are estimated via the maximum likelihood method. The asymptotic confidence intervals of the parameters, reliability function, hazard rate function and reversed hazard rate function are also obtained. A simulation study is carried out to evaluate the performance of the maximum likelihood estimates. In addition, three real data are applied to show the superiority of the xgamma-Burr XII distribution over some known distributions in real-life applications.

Cross-attribute level effects (CALE) model has demonstrated better predictive accuracy for out-of-sample health states than the conventional additive main-effects model in cross-validation analysis of the 5-level version of EQ-5D (EQ-5D-5L) composite time trade-off (cTTO) datasets. In this study, we aimed to further test the performance of CALE model using a different design and modified EQ-5D-5L states. A total of 29 EQ-5D-5L self-care bolt-off states, 30 EQ-5D-5L states, and 31 EQ-5D-5L vision bolt-on states were selected from the same orthogonal array. A total of 600 university students were interviewed face-to-face to value a subset of these health states using the cTTO method. For each type of health state, we fitted both the conventional main-effects model and the CALE model. Predictive accuracy was assessed in a series of cross-validation analysis using the leave-one-state-out method. Overall, the CALE model outperformed the conventional model for each of the 3 types of health states in predicting the cTTO values of out-of-sample health states. The prediction accuracy of using the CALE model improved with the number of dimensions in health states, for example, the MAE decreased about 24%, 67%, and 77% for the EQ-5D-5L self-care bolt-off, EQ-5D-5L, and EQ-5D-5L vision bolt-on states, respectively, when using CALE models. Our study supported the strengths of the CALE model for modelling the utility values of both original and modified EQ-5D-5L health states. Investigators with limited resources may consider using the CALE model to lower the costs for their valuation studies for EQ-5D-5L or similar health state descriptive systems. • In the past 3 decades, the modeling of EQ-5D value sets has been using the 20-parameter main-effects model. • Using the out-of-sample cross-validation method, we found that the cross-attribute level effects constantly outperform the additive model in modeling the original 5-level version of EQ-5D (EQ-5D-5L) and 2 types of modified EQ-5D-5L health state values. • This study used a small composite time trade-off design, which works well with the cross-attribute level effects model and should be considered lower the costs for future EQ-5D-5L valuation study. [ABSTRACT FROM AUTHOR]

Accurately predicting the price change of steel (main building materials) is an effective means to control and manage the cost of construction projects. It is one of the ways for construction enterprises to reasonably allocate building materials, save resources, reduce carbon emissions and reduce environmental pollution. Based on the monthly historical price data of 100 steel rebar (16 mm) from November 2010 to February 2019, the separation and retrieval process of the four components in the time series are improved. The improved multiplicative and additive models were used to make separate predictions, and the reasonable weight is given to combine the multiplication and addition model by the reciprocal of variance method. Finally, an improved prediction model of steel bar price combination with higher prediction accuracy is obtained. The prediction results show that the improved multiplication model and addition model have higher prediction accuracy, their MAPE are 2.62% and 2.36% respectively. Moreover, the prediction accuracy of the combined model is even higher, its MAPE is 2.29%. The prediction accuracy of the improved composite model is higher than that of the individual models. The improved combined prediction model of reinforcement price based on time series method can provide some reference and help for cost control and management in construction engineering, further reduce resource waste and construction non-point source pollution.

We focus on identifying genomics risk factors of higher body mass index (BMI) incorporating a priori information, such as biological pathways. However, the commonly used methods to incorporate prior information provide a model for the mean function of the outcome and rely on unmet assumptions. To address these concerns, we propose a method for nonparametric additive quantile regression with network regularization to incorporate the information encoded by known networks. To account for nonlinear associations, we approximate the unknown additive functional effect of each predictor with the expansion of a B‐spline basis. We implement the group Lasso penalty to obtain a sparse model. We define the network‐constrained penalty by the total ℓ2$$ {\ell}_2 $$ norm of the difference between the effect functions of any two linked genes in the known network. We further propose an efficient computation procedure to solve the optimization problem that arises in our model. Simulation studies show that our proposed method performs well in identifying more truly associated genes and less falsely associated genes than alternative approaches. We apply the proposed method to analyze the microarray gene‐expression dataset in the Framingham Heart Study and identify several 75 percentile BMI associated genes. In conclusion, our proposed approach efficiently identifies the outcome‐associated variables in a nonparametric additive quantile regression framework by leveraging known network information. [ABSTRACT FROM AUTHOR]

Data envelopment analysis (DEA) has been widely applied in measuring the efficiency of homogeneous decision-making units. Network DEA, as an important branch of DEA, was built to examine the internal structure of a system, whereas traditional DEA models regard a system as a 'black box'. However, only a few previous studies on parallel systems have considered the interdependent relationship between system components. In recent years, parallel interdependent processes systems commonly exist in production systems because of serious competition among organisations. Thus, an approach to measure the efficiency of such systems should be proposed. This paper builds an additive DEA model to measure a parallel interdependent processes system with two components which have an interdependent relationship. Then, the model is applied to analyse the '985 Project' universities in China, and certain policy implications are explained. [ABSTRACT FROM AUTHOR]

We define a novel class of additive models, called Extended Latent Gaussian Models, that allow for a wide range of response distributions and flexible relationships between the additive predictor and mean response. The new class covers a broad range of interesting models including multi-resolution spatial processes, partial likelihood-based survival models, and multivariate measurement error models. Because computation of the exact posterior distribution is infeasible, we develop a fast, scalable approximate Bayesian inference methodology for this class based on nested Gaussian, Laplace, and adaptive quadrature approximations. We prove that the error in these approximate posteriors is o p (1) under standard conditions, and provide numerical evidence suggesting that our method runs faster and scales to larger datasets than methods based on Integrated Nested Laplace Approximations and Markov chain Monte Carlo, with comparable accuracy. We apply the new method to the mapping of malaria incidence rates in continuous space using aggregated data, mapping leukemia survival hazards using a Cox Proportional-Hazards model with a continuously-varying spatial process, and estimating the mass of the Milky Way Galaxy using noisy multivariate measurements of the positions and velocities of star clusters in its orbit. for this article are available online. [ABSTRACT FROM AUTHOR]

This study develops an optimal frequentist model averaging approach for estimating the unknown conditional mean function in the nonparametric additive model when the covariates and the degree of smoothing are subject to uncertainty. Our weight choice criterion selects model weights by minimizing a plug-in estimator of the risk of the model average estimator under a squared error loss function. We derive the convergence rate of the model weights obtained from our proposed method to the infeasible optimal weights, and prove that the resultant model average estimators are asymptotically optimal. An extension to the additive autoregressive model for time series data is also considered. Our simulation analysis shows that the proposed model average estimators significantly outperform several commonly used model selection estimators and their model averaging counterparts in terms of the mean squared error in a large part of the parameter space. We further illustrate our methods using two real data studies. [ABSTRACT FROM AUTHOR]

This paper proposes the D-optimal design for the additive mixture model with two-response, which is linear model with no interaction terms. The optimality was validated by using the general equivalence theorem, and the corresponding weights are found under which additive model satisfies D-optimality. In addition, relevant statistics and graphics are given to illustrate our results.

The spread of coronavirus disease around the world has had an immense impact on most economic sectors. Yet amid the turmoil and chaos from the worldwide pandemic, one industry is thriving noticeably. The coronavirus disease is a once in a lifetime business opportunity for pharmaceutical companies. This study presents an artificial intelligence method composed of optimization and machine learning. Data envelopment analysis (DEA) is used to measure productivities and efficiencies of pharmaceutical companies during the COVID-19 pandemic using the additive model in window analysis, the BCC (Banker-Charnes-Cooper) model, and the CCR (Charnes-Cooper-Rhodes) model. The three models are assessed using DataStream financial information with research and development (R&D) investment. The results indicated the additive model's superiority in window analysis, followed by the BCC and CCR models. In the end, some of well-known data mining algorithms, based on the suggested data, have been evaluated in various tools to find the most efficient tool and algorithm.

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An additive power-transformed half-logistic distribution is proposed to model lifetime data having bathtub-shaped hazard rate function. The model is derived as the sum of hazard rates of two independently distributed power transformed half-logistic distributions. Some properties of the model like shapes of hazard function, quantile function, mean time between failure (MTBF), probability of failure due to early birth defect and due to ageing are discussed. The graphical estimation technique based on probability plotting procedure is demonstrated to be useful in estimating the parameters of this model. The method of maximum likelihood estimation is discussed for estimating the model parameters and simulation study is considered to show the performance of the estimates. Finally the usefulness of the developed distribution is illustrated by applying it to two real life datasets from engineering reliability. [ABSTRACT FROM AUTHOR]

Philippine COVID-19 data have so many gaps resulting from lack of mass testing, late reporting of test results, and unreported cases -- leading to too much noise and sparsity. A sparse spatial autoregressive model linking COVID-19 incidence and mortality rates to the healthcare system, demographic and economic indicators, disease prevalence, vaccination, urbanity, and environmental factors is proposed. The model allows for irregular spatial units accounting for temporal dependencies within a neighborhood was estimated using the Cochranne-Orcutt procedure embedded into the backfitting algorithm. Daily COVID-19 cases and deaths across provinces and cities in the National Capital Region (NCR) from 01 April 2020-15 September 2021 show a significant association of COVID-19 prevalence rate with the number of health workers, revenue of the local government unit (LGU), and prevalence of tuberculosis (TB). On the other hand, COVID-19 mortality rates are associated with the number of health workers, number of licensed COVID-19 testing laboratories, number of cities in an LGU, revenue of the LGU, prevalence rate of cancer, and prevalence rate of TB. The models emphasize the importance of resources available in the LGU that can boost the capabilities of the health care system. Pre-existing health conditions (co-morbidities) in the communities also determine the prevalence and mortality rates of COVID-19 in the Philippines. [ABSTRACT FROM AUTHOR]

Previous papers have mentioned that conditioning on a binary collider would introduce an association between its causes in at least 1 stratum. In this paper, we prove this statement and, along with intuitions, formally examine the direction and magnitude of the associations between 2 risk factors of a binary collider using interaction contrasts. Among level one of the collider, 2 variables are independent, positively associated, and negatively associated if multiplicative risk interaction contrast is equal to, more than, and less than 0, respectively; the same results hold for the other level of the collider if the multiplicative survival interaction contrast, equal to multiplicative risk interaction contrast minus the additive risk interaction contrast, is compared with 0. The strength of the association depends on the magnitude of the interaction contrast: The stronger the interaction is, the larger the magnitude of the association will be. However, the common conditional odds ratio under the homogeneity assumption will be bounded. A figure is presented that succinctly illustrates our results and helps researchers to better visualize the associations introduced upon conditioning on a collider. [ABSTRACT FROM AUTHOR]

Motivated by two examples concerning global warming and monthly total import and export by China, we study time series that contain a nonparametric periodic component with an unknown period, a nonparametric trending behavior and also additive covariate effects. Further, as the amplitude function may change at some known or unknown change-point(s), we extend our model to take this dynamical periodicity into account and introduce two change-point estimators. To the best of knowledge, this is the first work to study such complex periodic structure. A two-step estimation procedure is proposed to estimate accurately the periodicity, trend and covariate effects. First, we estimate the period with the trend and covariate effects being approximated by B-splines rather than being ignored. To achieve robustness we employ a penalized M-estimation method which uses post model selection inference ideas. Next, given the period estimate, we estimate the amplitude, trend and covariate effects. Asymptotic properties of our estimators are derived, including consistency of the period estimator and asymptotic normality and oracle property of the estimated periodic sequence, trend and covariate effects. Simulation studies confirm superiority of our method and illustrate good performance of our change-point estimators. Applications to the two motivating examples demonstrate utilities of our methods. [ABSTRACT FROM AUTHOR]

Purpose: Tobacco smoking and alcohol drinking are positively associated with psoriasis prevalence and disease severity. Researches focusing on the influence of smoking and drinking on the treatment efficacy of psoriasis are still limited, especially their interaction effect. This study aims to explore the interactive effects of smoking and drinking on the treatment efficacy in psoriasis patients., Patients and Methods: From 2021 to 2022, we recruited 560 patients with psoriasis from Shanghai Skin Diseases Hospital. Demographic and clinical features as well as treatment efficacy were collected through questionnaire interview and physical examination during patient's hospital visit at week 0, week 4 and week 8. Logistic regression model was used to explore the influence of smoking and drinking on the treatment efficacy in psoriasis patients, and multiplicative and additive interaction models were used to verify the interaction effect of smoking and drinking on the treatment efficacy., Results: The prevalence of smoking and drinking among psoriasis patients was respectively 43.8% and 25.4%, and 19.6% of them with both smoking and drinking. Logistic regression analysis showed that patients with smoking (OR=7.78, 95% CI: 5.26~11.49) and drinking (OR=5.21, 95% CI: 3.29~8.27) had higher risk of experiencing the failure to achieve PASI 75 at week 8, even with the adjustment of confounders. Moreover, multiplicative as well as additive model showed that tobacco smoking interacted with alcohol drinking which influenced the treatment efficacy more severely (OR=12.74, 95% CI: 7.16~22.67). The proportion of PASI 75 achievement in female patients (OR=19.54) and patients with methotrexate (OR=28.31) and biologics (OR=21.61) were more likely being affected by smoking and drinking., Conclusion: Tobacco smoking and alcohol drinking could increase the failure of PASI 75 achievement in patients with psoriasis, individually and interactively. We recommend that dermatologists should educate patients to pay attention to the negative effects of smoking and drinking, encourage them to quit, and thus improve the treatment efficacy., Competing Interests: The authors report no conflicts of interest in this work., (© 2024 Shen et al.)