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Administrative databases have become an increasingly popular data source for population-based health research. We explore how mortality risk is associated with some health service utilization process via linked administrative data. A generalized Cox regression model is proposed using a time-dependent stratification variable to summarize lifetime service utilization. Recognizing the service utilization over time as an internal covariate in the survival analysis, conventional likelihood methods are inapplicable. We present an estimating function based procedure for estimating model parameters, and provide a testing procedure for updating the stratification levels. The proposed approach is examined both asymptotically and numerically via simulation. We motivate and illustrate the proposed approach using an on-going program pertaining to opioid agonist treatment (OAT) management for individuals identified with opioid use disorders. Our analysis of the OAT data indicates that the OAT effect on mortality risk decreases in successive OAT attempts, in which two risk classes based on an individual's treatment episode number are established: one with 1–3 OAT episodes, and the other with 4+ OAT episodes. [ABSTRACT FROM AUTHOR]

Summary: We address the problem of performing inference on the linear and nonlinear terms of a semiparametric spatial regression model with differential regularisation. For the linear term, we propose a new resampling procedure, based on (partial) sign‐flipping of an appropriate transformation of the residuals of the model. The proposed resampling scheme can mitigate the bias effect induced by the differential regularisation. We prove that the proposed test is asymptotically exact. Moreover, we show, by simulation studies, that it enjoys very good control of Type‐I error also in small sample scenarios, differently from parametric alternatives. Additionally, we show that the proposed test has higher power with respect than recently proposed nonparametric tests on the linear term of semiparametric regression models with differential regularisation. Concerning the nonlinear term, we develop three different inference approaches: a parametric one and two nonparametric alternatives. The nonparametric tests are based on a sign‐flip approach. One of these is proved to be asymptotically exact, while the other is proved to be exact also for finite samples. Simulation studies highlight the good control of Type‐I error of the nonparametric approaches with respect the parametric test, while retaining high power. [ABSTRACT FROM AUTHOR]

In semiparametric regression, traditional methods such as mixed generalized additive models (GAM), computed via Laplace approximation or variational approximation using penalized marginal likelihood estimation, may not achieve sparsity and unbiasedness simultaneously, and may sometimes suffer from convergence problems. To address these issues, we propose an estimator for semiparametric generalized additive models based on the marginal likelihood. Our approach provides sparsity estimates and allows for statistical inference. To estimate and select variables, we use the smoothly clipped absolute deviation penalty (SCAD) within the framework of variational approximation. We also propose efficient iterative algorithms to obtain estimations. Simulation results support our theoretical characteristics, and we demonstrate that our method is more effective than the original variational approximations framework and many other penalized methods under certain conditions. Moreover, applications with actual data further demonstrate the superior performance of the proposed method. [ABSTRACT FROM AUTHOR]

Modelling multivariate survival data is complicated by the complex association structure among the responses. To balance model flexibility and interpretability, we propose a semiparametric copula model to modulate multivariate survival data, with the marginal distributions of the response components described by semiparametric linear transformation models. To conduct inference about the model parameters, we develop a two‐stage maximum likelihood method and a three‐stage pseudo‐likelihood estimation procedure. We investigate the impact of model misspecification on the estimation of covariate effects and identify a scenario in which consistent estimation of the marginal parameters is retained even when the copula model is misspecified. The proposed methods are justified both theoretically and empirically. An application to a real dataset is provided to demonstrate the utility of the proposed method. [ABSTRACT FROM AUTHOR]

Most existing copula models for dependent censoring in the literature assume that the parameter defining the copula is known. However, prior knowledge on this dependence parameter is often unavailable. In this article we propose a novel model under which the copula parameter does not need to be known. The model is based on a parametric copula model for the relation between the survival time (T) and the censoring time (C), whereas the marginal distributions of T and C follow a semiparametric Cox proportional hazards model and a parametric model, respectively. We show that this model is identified, and propose estimators of the nonparametric cumulative hazard and the finite-dimensional parameters. It is shown that the estimators of the model parameters and the cumulative hazard function are consistent and asymptotically normal. We also investigate the performance of the proposed method using finite-sample simulations. Finally, we apply our model and estimation procedure to a follicular cell lymphoma dataset. for this article are available online. [ABSTRACT FROM AUTHOR]

Markov chain Monte Carlo (MCMC)-based simulation approaches are by far the most common method in Bayesian inference to access the posterior distribution. Recently, motivated by successes in machine learning, variational inference (VI) has gained in interest in statistics since it promises a computationally efficient alternative to MCMC enabling approximate access to the posterior. Classical approaches such as mean-field VI (MFVI), however, are based on the strong mean-field assumption for the approximate posterior where parameters or parameter blocks are assumed to be mutually independent. As a consequence, parameter uncertainties are often underestimated and alternatives such as semi-implicit VI (SIVI) have been suggested to avoid the mean-field assumption and to improve uncertainty estimates. SIVI uses a hierarchical construction of the variational parameters to restore parameter dependencies and relies on a highly flexible implicit mixing distribution whose probability density function is not analytic but samples can be taken via a stochastic procedure. With this paper, we investigate how different forms of VI perform in semiparametric additive regression models as one of the most important fields of application of Bayesian inference in statistics. A particular focus is on the ability of the rivalling approaches to quantify uncertainty, especially with correlated covariates that are likely to aggravate the difficulties of simplifying VI assumptions. Moreover, we propose a method, where we combine both advantages of MFVI and SIVI and compare its performance. The different VI approaches are studied in comparison with MCMC in simulations and an application to tree height models of douglas fir based on a large-scale forestry data set. [ABSTRACT FROM AUTHOR]

Deep learning has enjoyed tremendous success in a variety of applications but its application to quantile regression remains scarce. A major advantage of the deep learning approach is its flexibility to model complex data in a more parsimonious way than nonparametric smoothing methods. However, while deep learning brought breakthroughs in prediction, it is not well suited for statistical inference due to its black box nature. In this article, we leverage the advantages of deep learning and apply it to quantile regression where the goal is to produce interpretable results and perform statistical inference. We achieve this by adopting a semiparametric approach based on the partially linear quantile regression model, where covariates of primary interest for statistical inference are modeled linearly and all other covariates are modeled nonparametrically by means of a deep neural network. In addition to the new methodology, we provide theoretical justification for the proposed model by establishing the root-n consistency and asymptotically normality of the parametric coefficient estimator and the minimax optimal convergence rate of the neural nonparametric function estimator. Across several simulated and real data examples, the proposed model empirically produces superior estimates and more accurate predictions than various alternative approaches. [ABSTRACT FROM AUTHOR]

The fastest-growing fields of research in the modern world include social networks and their associated studies. This field is famed for being a highly effective way to investigate interpersonal connections. We tried to implement a creative strategy to improve institutional administrative activities. If we need to introduce a new concept within our organization, we have many alternatives, one of which is a "train the trainer" program. In an attempt to train almost the whole staff of an institution about software, we tried to understand the interactions between the staff members as a network. We use semi-parametric regression analysis to identify the covariates affecting each person's training. It enables us to examine how various factors impact the probability of receiving training at a specific time. [ABSTRACT FROM AUTHOR]

The human microbiome plays an important role in our health and identifying factors associated with microbiome composition provides insights into inherent disease mechanisms. By amplifying and sequencing the marker genes in high-throughput sequencing, with highly similar sequences binned together, we obtain operational taxonomic units (OTUs) profiles for each subject. Due to the high-dimensionality and nonnormality features of the OTUs, the measure of diversity is introduced as a summarization at the microbial community level, including the distance-based beta-diversity between individuals. Analyses of such between-subject attributes are not amenable to the predominant within-subject-based statistical paradigm, such as t-tests and linear regression. In this paper, we propose a new approach to model beta-diversity as a response within a regression setting by utilizing the functional response models (FRMs), a class of semiparametric models for between- as well as within-subject attributes. The new approach not only addresses limitations of current methods for beta-diversity with cross-sectional data, but also provides a premise for extending the approach to longitudinal and other clustered data in the future. The proposed approach is illustrated with both real and simulated data.

Regression analysis is one of the statistical methods used to model the relationship between response variables and predictor variables. Semiparametric regression is a combination of parametric and nonparametric regression. The estimator used in estimating the semiparametric regression model in this research is the Local Polynomial. Longitudinal data can be found in the health sector, including dengue hemorrhagic fever (DHF) data. The laboratory criteria for indication of DHF is thrombocytopenia. This research aims to obtain platelets model for DHF patients that can be used for forecasting so that it is hoped that it can provide information to the medical team in treating DHF patients. The estimated model used is Local Polynomial semiparametric regression on longitudinal data. The response variables in this research were platelets of DHF patients, which were influenced by hemoglobin as parametric predictor variable and examination time while hospitalized as nonparametric predictor variable. In the local polynomial regression model, it is necessary to select the optimal bandwidth and polynomial order method, GCV. The optimum bandwidth selection based on the GCV method obtained is 1.5 and polynomial order of 2, then applied to DHF patient platelet data, producing an estimated local polynomial semiparametric regression model that follows the actual data pattern. Modeling the platelets of DHF patients obtained using a local polynomial estimator resulted in an R2 value of 84.25% and MAPE of 4.5%, indicating highly accurate forecasting, so it can be concluded that the resulting model is better at predicting.

In this paper, we consider the kernel method to estimate sliced average variance estimation (SAVE). SAVE is a tool as SIR (sliced inverse regression), recommended to identify and to estimate the central dimension reduction (CDR) subspace. CDR subspace is the intersection of all dimension reduction subspaces which are at the base to describe the conditional distribution of the response Y given a d − dimensional predictor vector X. SAVE and even SIR are used to estimate CDR subspace. Two versions are very popular: slice version and kernel version. In this paper, we are looking at the kernel version. For Kernel SAVE version, two asymptotic properties have been demonstrated in particular: asymptotic normality and convergence in probability. And we know that these two properties, although important, are weak in front of almost sure convergence. However, until now, the strong consistency has not yet been obtained. In this paper, we obtain, under weaker assumptions, this asymptotic property. [ABSTRACT FROM AUTHOR]

Real estate valuation is typically based on hedonic regression models where the expected price of a property is explained in dependence of its attributes. However, investors in the housing market are equally interested in the distribution of real estate market values (including price variation), that is, determining the impact of attributes of a property on the entire conditional distribution. We therefore consider Bayesian structured additive distributional and quantile regression models for real estate valuation. In the first approach, each parameter of a potentially complex parametric response distribution is related to a structured additive predictor. In contrast, the second approach proceeds differently and models arbitrary quantiles of the response distribution directly and nonparametrically. Both models presented are based on a multilevel version of structured additive regression thereby utilizing the typical hierarchical structure of real estate data. We demonstrate the proposed methodology within a detailed case study based on more than 3 000 owner-occupied single family homes in Austria, discuss interpretation of the resulting effect estimates, and compare models based on their predictive ability. [ABSTRACT FROM AUTHOR]

We study the emergence of a new population of firms and show that legitimation is a local phenomenon and stimulates entry. However, legitimation is offset by competition as the number of firms increases. We provide evidence that the legitimation and competition effects within the municipality dominate the effects in a larger area. We also give a methodological contribution and show that the magnitude of the legitimation and competition effects is weaker than what is predicted by the conventional regressions. Finally, legitimation and competition do not act 'symmetrically': legitimation is stronger and acts more quickly than the competitive effect. [ABSTRACT FROM AUTHOR]

The Botswana Combination Prevention Project was a cluster‐randomized HIV prevention trial whose follow‐up period coincided with Botswana's national adoption of a universal test and treat strategy for HIV management. Of interest is whether, and to what extent, this change in policy modified the preventative effects of the study intervention. To address such questions, we adopt a stratified proportional hazards model for clustered interval‐censored data with time‐dependent covariates and develop a composite expectation maximization algorithm that facilitates estimation of model parameters without placing parametric assumptions on either the baseline hazard functions or the within‐cluster dependence structure. We show that the resulting estimators for the regression parameters are consistent and asymptotically normal. We also propose and provide theoretical justification for the use of the profile composite likelihood function to construct a robust sandwich estimator for the variance. We characterize the finite‐sample performance and robustness of these estimators through extensive simulation studies. Finally, we conclude by applying this stratified proportional hazards model to a re‐analysis of the Botswana Combination Prevention Project, with the national adoption of a universal test and treat strategy now modeled as a time‐dependent covariate. [ABSTRACT FROM AUTHOR]

Besides censoring, multicollinearity and outliers are two common problem in regression analysis. In this paper we propose a family of robust ridge estimators for the censored semiparametric regression models. The proposed robust estimators is based on least trimmed squares (LTS) method. This method is insensitive to corruption due to outliers, provided that the outliers constitute less than 50 % of the set, in other words, LTS is a robust estimator with a 50 % breakdown point. The FAST-LTS algorithm is developed for the computation of the estimators. Furthermore, a robust method for the estimate of shrinkage parameters is suggested. Monté-Carlo simulation study demonstrates the merit of the new method in the aspect of solving the multicollinearity and sensitivity to outliers over the ordinary least squares estimation. Finally, an example of real data is given for illustration. [ABSTRACT FROM AUTHOR]

In this study, we leverage geographical coordinates and firm-level panel data to uncover variations in production across different locations. Our approach involves using a semiparametric proxy variable regression estimator, which allows us to define and estimate a customized production function for each firm and its corresponding location. By employing kernel methods, we estimate the nonparametric functions that determine the model's parameters based on latitude and longitude. Furthermore, our model incorporates productivity components that consider various factors that influence production. Unlike spatially autoregressive-type production functions that assume a uniform technology across all locations, our approach estimates technology and productivity at both the firm and location levels, taking into account their specific characteristics. To handle endogenous regressors, we incorporate a proxy variable identification technique, distinguishing our method from geographically weighted semiparametric regressions. To investigate the heterogeneity in production technology and productivity among Norwegian grain farmers, we apply our model to a sample of farms using panel data spanning from 2001 to 2020. Through this analysis, we provide empirical evidence of regional variations in both technology and productivity among Norwegian grain farmers. Finally, we discuss the suitability of our approach for addressing the heterogeneity in this industry. [ABSTRACT FROM AUTHOR]

We develop the quadratic inference function method to the parameter estimation of the marginal models with general relative risk form and common baseline hazard function for multivariate failure time data. The usual exponential relative risk form is relaxed to an non-negative twice differentiable form. The proposed estimator, under some regularity conditions, is shown to be consistent and asymptotically normal with a covariance matrix that can be consistently estimated. In addition, a test statistic based on the quadratic inference function for the parameter inference has been provided. The simulation results show that the proposed method taking the correlation between the failure times into the estimation procedure gains more efficiency than the method using the independent structure when the correlation cannot be ignored, and can easily deal with the situation when the cluster size is large. The proposed method is illustrated by analysis of a real data from the Diabetic Retinopathy Study. [ABSTRACT FROM AUTHOR]

We introduce a bivariate bidimensional mixed‐effects regression model, motivated by the analysis of CO2$$ {\mathrm{CO}}_2 $$ emission levels and growth on OECD countries from 1990 to 2018. The model is able to capture heterogeneity across countries and allows for a full association structure among outcomes, assuming a discrete distribution for the random terms with a possibly different number of support points in each univariate profile. We test the behavior of the proposed approach via a simulation study, considering several factors such as the number of observed units, times, and levels of heterogeneity in the data. Empirically, we define an extended version of the STIRPAT model where all model parameters, and not only the mean, vary according to a regression model. Our empirical findings provide evidence of heterogeneous behaviors across countries and suggest the need of a flexible approach to properly reflect the heterogeneity in both the emission levels and the growth processes. [ABSTRACT FROM AUTHOR]

We propose a semiparametric regression defined from the generalized odd log-logistic Maxwell distribution under the cubic spline with linear and nonlinear effects for censored and uncensored data. The parameter estimates for this regression are determined using penalized likelihood. Global influence diagnostics and quantile residuals are addressed in new standards. Several Monte Carlo simulations investigate the consistency of the estimates. The usefulness of the new regression is illustrated by means of two applications to real data. [ABSTRACT FROM AUTHOR]

This paper aims to present an overview of Semiparametric additive models. An estimation of the finite‐parameters of semiparametric regression models that involve additive nonparametric components is explained, including their historical background. In addition, three different smoothing techniques are considered in order to show the working procedures of the estimators and to explore their statistical properties: smoothing splines, kernel smoothing and local linear regression. These methods are compared with respect to both their theoretical and practical behaviors. A simulation study and a real data example are carried out to reveal the performances of the three methods. Accordingly, the advantages and disadvantages of each method regarding semiparametric additive models are presented based on their comparative scores using determined evaluation metrics for loss of information. This article is categorized under:Statistical Learning and Exploratory Methods of the Data Sciences > Modeling MethodsStatistical and Graphical Methods of Data Analysis > Multivariate AnalysisStatistical Models > Semiparametric Models [ABSTRACT FROM AUTHOR]

We develop a model‐based boosting approach for multivariate distributional regression within the framework of generalized additive models for location, scale, and shape. Our approach enables the simultaneous modeling of all distribution parameters of an arbitrary parametric distribution of a multivariate response conditional on explanatory variables, while being applicable to potentially high‐dimensional data. Moreover, the boosting algorithm incorporates data‐driven variable selection, taking various different types of effects into account. As a special merit of our approach, it allows for modeling the association between multiple continuous or discrete outcomes through the relevant covariates. After a detailed simulation study investigating estimation and prediction performance, we demonstrate the full flexibility of our approach in three diverse biomedical applications. The first is based on high‐dimensional genomic cohort data from the UK Biobank, considering a bivariate binary response (chronic ischemic heart disease and high cholesterol). Here, we are able to identify genetic variants that are informative for the association between cholesterol and heart disease. The second application considers the demand for health care in Australia with the number of consultations and the number of prescribed medications as a bivariate count response. The third application analyses two dimensions of childhood undernutrition in Nigeria as a bivariate response and we find that the correlation between the two undernutrition scores is considerably different depending on the child's age and the region the child lives in. [ABSTRACT FROM AUTHOR]

The dynamicity of functional (curve) markers from modern clinical studies offers deeper insights into complex disease physiology. A frequent clinical practice is to examine various 'pharmacokinetic features' of functional markers (definite integral, maximum value, time to maximum, etc.) that reflect important physiological underpinnings. For instance, the current diagnostic procedure for kidney obstruction is to examine several pharmacokinetic features of renogram curves characterizing renal function. Motivated by such clinical practices, we develop a statistical framework for evaluating diagnostic accuracy of pharmacokinetic features using area under the receiver operating characteristic curve (AUC). The major challenge is that functional markers are observed at discrete time points with measurement error. To address this challenge, we develop a two-stage non-parametric AUC estimator based on summary functionals providing unified representation of various pharmacokinetic features and study its asymptotic properties. We also propose a sensible adaptation of a semiparametric regression model that can describe heterogeneity of AUC across different subpopulations, while appropriately handling discreteness and noise in observed functional markers. Here, a novel data-driven approach that balances between bias and efficiency of the regression coefficient estimates is introduced. Finally, the framework is applied to rigorously evaluate pharmacokinetic features of renogram curves potentially useful for detecting kidney obstruction. [ABSTRACT FROM AUTHOR]

Wavelet analysis is one of the useful techniques in mathematics which is used much in statistics science recently. In this paper, in addition to introduce the wavelet transformation, the wavelet threshold estimation of semiparametric regression model with correlated errors with having Gaussian distribution is determined and the convergence ratio of estimator computed. To evaluate the wavelet threshold estimation, the block function and sinusoidal function are used as objective functions and using the simulation method the average of mean square error and standard deviation of this estimator are compared with the average of mean square error and standard deviation of kernel method. Also, the wavelet semiparametric regression model has been fitted to data on the growth rate of the teeth.

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Sliced inverse regression (SIR) is a recommended method to identify and estimate the central dimension reduction (CDR) subspace. CDR subspace is at the base to describe the conditional distribution of the response Y given a d-dimensional predictor vector X. To estimate this space, two versions are very popular: the slice version and the kernel version. A recursive method of the slice version has already been the subject of a systematic study. In this paper, we propose to study the kernel version. It's a recursive method based on a stochastic approximation algorithm of the kernel version. The asymptotic normality of the proposed estimator is also proved. A simulation study that not only shows the good numerical performance of the proposed estimate and which also allows to evaluate its performance with respect to existing methods is presented. A real dataset is also used to illustrate the approach. [ABSTRACT FROM AUTHOR]

The multiresponse semiparametric regression (MSR) model is a regression model with more than two response variables that are mutually correlated, and its regression function is composed of parametric and nonparametric components. The study objectives are propose a new method for estimating the MSR model using smoothing spline. Also, find the confidence interval (CI) of parameters and the distribution asymptotically of the model parameters estimator. Methods used in this study are reproducing kernel Hilbert space (RKHS) method and a developed penalized weighted least squares (PWLS), and apply pivotal quantity, central limit theorem, and theorems of Cramer-Wold and Slutsky. The results are an 100(1–α)% CI estimate and an asymptotic normal distribution for the parameters of the MSR model. In conclusion, the estimated MSR model is a combined components estimate of parametric and nonparametric which is linear to observation, and CIs of parameters depend on t distribution and estimator of parameters is asymptotically normally distributed. Future time, this study results can be used as theoretical bases to design standard growth charts of the toddlers which can then be used to assess the nutritional status of the toddlers.

The response variable of the regression analysis has a linear relationship with one of the variable predictors, however the unknown relationship pattern with the other predictor variables. Consequently, it can be approached by using semiparametric regression model. The predictor variable that has a linear relationship with the response variable can be approached by using linear parametric curve called parametric component. Meanwhile, the unknown relationship between the response variable with another predictor variable can be approached by using nonparametric curve called nonparametric component. If the predictor variable in nonparametric component is more than one, then it can be approached by using a different nonparametric curve named combined or mixed estimator. In this research, nonparametric component is approached using mixed estimator of multivariable linear truncated spline and multivariable kernel. The objective of this research is to estimate the model of semiparametric regression curve with mixed estimator of multivariable truncated spline and multivariable kernel. Estimation of this mixed model using ordinary least square method.

The robust Poisson method is becoming increasingly popular when estimating the association of exposures with a binary outcome. Unlike the logistic regression model, the robust Poisson method yields results that can be interpreted as risk or prevalence ratios. In addition, it does not suffer from frequent nonconvergence problems such as the most common implementations of maximum likelihood estimators of the log-binomial model. However, using a Poisson distribution to model a binary outcome may seem counterintuitive. Methodologic papers have often presented this as a good approximation to the more natural binomial distribution. In this article, we provide an alternative perspective to the robust Poisson method based on the semiparametric theory. This perspective highlights that the robust Poisson method does not require assuming a Poisson distribution for the outcome. In fact, the method only assumes a log-linear relation between the risk or prevalence of the outcome and the explanatory variables. This assumption and the consequences of its violation are discussed. We also provide suggestions to reduce the risk of violating the modeling assumption. Additionally, we discuss and contrast the robust Poisson method with other approaches for estimating exposure risk or prevalence ratios. See video abstract at, http://links.lww.com/EDE/B987 . [ABSTRACT FROM AUTHOR]

Due to difficulties in accessing detailed energy modelling and usage data that are required to estimate energy needs that are responsive to local circumstances, we propose an equivalence scale approach to the determination of required energy consumption. Our method requires the estimation of energy equivalence scales that are used to rescale reference household energy consumption and, thus, yield household-specific energy requirements. We apply the method using readily available income and expenditure data, finding that estimated required energy is well above actual energy expenditure for low- and middle-income households, which is consistent with an expectation that basic energy needs for poor households may not be met. The proposed approach is general enough to incorporate other features that might be deemed relevant and available in other settings, and, therefore, can be used to examine the affordability component of SDG 7, undertake energy poverty analysis or design appropriate policies. [ABSTRACT FROM AUTHOR]

Gradually increasing durations of high temperature caused by climate change harm the health of individuals and then lead to death. This study aimed to investigate the relationship between durations of different daily mean air-temperature categories and mortality in China and forecast future mortality changes in China for 2020–2050 under Representative Concentration Pathways (RCP)4.5 and RCP8.5 scenarios. The daily mean air temperature was divided into 10 categories, and the days under each air-temperature category were counted during the period of 2000–2015. Then, the connection between the days of each of the 10 air-temperature categories and mortality was established using the semi parametric regression model. Results indicate that the days of the >32 °C category have the largest impact on mortality in China, with the death rate increasing by 23‰ for one additional day. Predictions reveal that mortality in China will increase 25.48% and 26.26% under the RCP4.5 and RCP8.5 scenarios, respectively. Moreover, the mortality of 86 regions in western China will increase 30.42%. Therefore, in the future, the increasing duration of days of high temperatures will raise the mortality rate in China and aggravate the mortality gap between developed and underdeveloped regions. [ABSTRACT FROM AUTHOR]

Infectious diseases such as severe acute respiratory syndrome (SARS) and influenza are influenced by weather conditions. Climate variables, for example, temperature and humidity, are two important factors in the severity of COVID-19's impact on the human respiratory system. This study aims to examine the effects of these climate variables on COVID-19 mortality. The data are collected from March 08, 2020, to April 30, 2022. The parametric regression under GAM and semiparametric regression under GAMLSS frameworks are used to analyze the daily number of death due to COVID-19. Our findings revealed that temperature and relative humidity are commencing to daily deaths due to COVID-19. A positive association with COVID-19 daily death counts was observed for temperature range and a positive association for humidity. In addition, one-unit increase in daily temperature range was only associated with a 1.08% (95% CI: 1.06%, 1.10%), and humidity range was only associated with a 1.03% (95% CI: 1.02%, 1.03%) decrease in COVID-19 deaths. A flexible regression model within the framework of Generalized Additive Models for Location Scale and Shape is used to analyze the data by adjusting the time effect. We used two adaptable predictor models, such as (i) the Fractional polynomial model and (ii) the B-spline smoothing model, to estimate the systematic component of the GAMLSS model. According to both models, high humidity and temperature significantly (and drastically) lessened the severity of COVID-19 death. The findings on the epidemiological trends of the COVID-19 pandemic and weather changes may interest policymakers and health officials. [ABSTRACT FROM AUTHOR]

In this study, we leverage geographical coordinates and firm-level panel data to uncover variations in production across different locations. Our approach involves using a semiparametric proxy variable regression estimator, which allows us to define and estimate a customized production function for each firm and its corresponding location. By employing kernel methods, we estimate the nonparametric functions that determine the model’s parameters based on latitude and longitude. Furthermore, our model incorporates productivity components that consider various factors that influence production. Unlike spatially autoregressive-type production functions that assume a uniform technology across all locations, our approach estimates technology and productivity at both the firm and location levels, taking into account their specific characteristics. To handle endogenous regressors, we incorporate a proxy variable identification technique, distinguishing our method from geographically weighted semiparametric regressions. To investigate the heterogeneity in production technology and productivity among Norwegian grain farmers, we apply our model to a sample of farms using panel data spanning from 2001 to 2020. Through this analysis, we provide empirical evidence of regional variations in both technology and productivity among Norwegian grain farmers. Finally, we discuss the suitability of our approach for addressing the heterogeneity in this industry.

Regression analysis aims to determine the relationship between response variables and predictor variables. There are three approaches to estimate regression curves, there are parametric, nonparametric, and semiparametric regression. In this study, the form of spline semiparametric regression curve estimator for longitudinal data assessed. Based on the estimator that be obtained by using Weighted Least Square (WLS) optimization applied to model electricity consumption in Madura by choosing a model for longitudinal data based on linear spline estimator with two knot. The good criterion of the model is using the GCV value, the coefficient of determination and the value of MSE. The best model is a model that has a high coefficient of determination and a small MSE value. This spline model has a determination coefficient value of 99,72911% and MSE 32,50458.

The Cox proportional hazards model is commonly used to estimate the association between time-to-event and covariates. Under the proportional hazards assumption, covariate effects are assumed to be constant in the follow-up period of study. When measurement error presents, common estimation methods that adjust for an error-contaminated covariate in the Cox proportional hazards model assume that the true function on the covariate is parametric and specified. We consider a semiparametric partly linear Cox model that allows the hazard to depend on an unspecified function of an error-contaminated covariate and an error-free covariate with time-varying effect, which simultaneously relaxes the assumption on the functional form of the error-contaminated covariate and allows for nonconstant effect of the error-free covariate. We take a Bayesian approach and approximate the unspecified function by a B-spline. Simulation studies are conducted to assess the finite sample performance of the proposed approach. The results demonstrate that our proposed method has favorable statistical performance. The proposed method is also illustrated by an application to data from the AIDS Clinical Trials Group Protocol 175. [ABSTRACT FROM AUTHOR]

This paper investigates the implications for portfolio theory of using multivariate semiparametric estimators and a copula-based approach, especially when the number of risky assets becomes substantial. Parametric, nonparametric, and semiparametric regression methods are compared to approximate their returns in large-scale portfolio selection problems. Semiparametric regression models are used to prove that, under certain assumptions, the variability of the errors decreases as the number of factors increases. Moreover, a copula principal component analysis (PCA)-based approach is proposed, and its superiority to the classical Pearson PCA approach is demonstrated. Empirical analyses validate the suggested approaches and evaluate the impact of different approximation methods on portfolio selection problems. Here, the ex-ante sample paths of several portfolio strategies aiming to maximize portfolio wealth using either reward-risk or drawdown-based performance measures are compared. The results show that the proposed methodologies outperform the traditional approach for out-of-sample portfolios, especially when the dependence structure is represented by the Pearson linear correlation. [ABSTRACT FROM AUTHOR]

With increasingly abundant data that relate to both space and time becoming available, spatiotemporal modeling is receiving much attention in the literature. This paper study develops a class of spatiotemporal autoregressive partially linear varying-coefficient models that are sufficiently exible to simultaneously capture the spatiotemporal dependence and nonstationarity often encountered in practice. When spatial observations are observed over time and exhibit dynamic and nonstationary behaviors, our models become particularly useful. We develop a numerically stable and computationally efficient estimation procedure, using the tensor-product splines over triangular prisms to approximate the coefficient functions. The estimators of both the constant coefficients and the varying coefficients are consistent. We also show that the estimators of the constant coefficients are asymptotically normal, which enables us to construct confidence intervals and make inferences. The method's performance is evaluated using Monte Carlo experiments, and applied to model and forecast the spread of COVID-19 at the county level in the United States. [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]

Time series data are often observed in ecological monitoring. Frequently, such data exhibit nonlinear trends over time potentially due to complex relationships between observed and auxiliary variables, and there may also be sudden declines over time due to major disturbances. This poses substantial challenges for modeling such data and also for adaptive monitoring. To address this, we propose methods for finding adaptive designs for monitoring in such settings. This work is motivated by a monitoring program that has been established at Scott Reef; a coral reef off the Western coast of Australia. Data collected for monitoring the health of Scott Reef are considered, and semiparametric and interrupted time series modeling approaches are adopted to describe how these data vary over time. New methods are then proposed that enable adaptive monitoring designs to be found based on such modeling approaches. These methods are then applied to find future monitoring designs at Scott Reef where it was found that future information gain is expected to be similar across a variety of different sites, suggesting that no particular location needs to be prioritized at Scott Reef for the next monitoring phase. In addition, it was found that omitting some sampling sites/reef locations was possible without substantial loss in expected information gain, depending upon the disturbances that were observed. The resulting adaptive designs are used to form recommendations for future monitoring in this region, and for reefs where changes in the current monitoring practices are being sought. As the methods used and developed throughout this study are generic in nature, this research has the potential to improve ecological monitoring more broadly where complex data are being collected over time. [ABSTRACT FROM AUTHOR]

The semiparametric proportional odds (PO) model is a popular alternative to Cox's proportional hazards model for analyzing survival data. Although many approaches have been proposed for this topic in the literature, most of the existing approaches have been found computationally expensive and difficult to implement. In this article, a novel and easy-to-implement approach based on an expectation-maximization (EM) algorithm is proposed for analyzing right-censored data. The EM algorithm involves only solving a low-dimensional estimating equation for the regression parameters and then updating the spline coefficients in simple closed form at each iteration. Our method is robust to initial values, converges fast, and provides the variance estimates in closed form. Simulation studies suggest that the proposed method has excellent performance in estimating both regression parameters and the baseline survival function, even when the right censoring rate is very high. The method is applied to a large dataset about breast cancer survival extracted from the Surveillance, Epidemiology, and End Results (SEER) database maintained by the U.S. National Cancer Institute. This method is now available in R package regPOr for public use. [ABSTRACT FROM AUTHOR]

Coal mining is the main factor causing China’s CO2 emissions. However, the composition of CO2 emissions caused by coal mining is still unclear. In this study, we developed a detection method that combines climatologically homogeneous region recognition and semiparametric regression to quantitatively calculate the contribution of human activities and climate to CO2 emissions. On the basis of determining the composition of CO2 emissions in Shanxi coalfield, we calculated the value of four CO2 emissions components (coal mining, climate, other disturbances and random fluctuation), analyzed their temporal and spatial regulation, and discussed the relationship between each component and land use. The results show that the coal mining component accounted for the largest proportion, 81 % of the CO2 emissions, and the climate component accounted for the smallest proportion. From 2000 to 2019, the CO2 emissions and coal mining component in the study area showed a trend of first increasing and then decreasing, reaching maximum monthly values of 65.5 t and 55.6 t in 2009 and declining in 2010 and 2018, respectively. The components of CO2 emissions were strongly correlated with the land cover types. Forestland had the lowest climate component (-0.35 t), as well as other disturbances component (7.37 t). Farmland had the lowest coal mining component (20.14 t). Water and built-up land had the highest value of each component. In the future, the climate component of farms in the study area will show an upward trend, while forestland, grassland and water will show a downward trend. The detection methods of CO2 emission components can be incorporated into the ecosystem model to improve the governance and protection ability of the ecological environment.

Theories and empirical evidence document the importance of early life environmental factors on later life cognition. A next question is how and in what dimension associations between early life environments and later life cognition vary. Using data from the UK Biobank in conjunction with time-place-specific infant mortality rates, we assessed heterogeneous and non-linear associations between early life conditions and later life cognition. We found that the association between the infant mortality rate and later life cognition increased once the UK achieved very low infant mortality rates, suggesting that additional decreases in infant mortality rates in an industrialized society continue to improve later life cognition. We also found that infant mortality rates have stronger effects at upper quantiles of the cognition distribution. This implies that adverse early life environments may have an important role for an early manifestation of cognitive aging.

Accurate and interpretable forecasting models predicting spatially and temporally fine-grained changes in the numbers of intrastate conflict casualties are of crucial importance for policymakers and international non-governmental organizations (NGOs). Using a count data approach, we propose a hierarchical hurdle regression model to address the corresponding prediction challenge at the monthly PRIO-grid level. More precisely, we model the intensity of local armed conflict at a specific point in time as a three-stage process. Stages one and two of our approach estimate whether we will observe any casualties at the country- and grid-cell-level, respectively, while stage three applies a regression model for truncated data to predict the number of such fatalities conditional upon the previous two stages. Within this modeling framework, we focus on the role of governmental arms imports as a processual factor allowing governments to intensify or deter from fighting. We further argue that a grid cell's geographic remoteness is bound to moderate the effects of these military buildups. Out-of-sample predictions corroborate the effectiveness of our parsimonious and theory-driven model, which enables full transparency combined with accuracy in the forecasting process. Los modelos de previsión precisos e interpretables que predicen los cambios a nivel espacial y temporal en la cantidad de víctimas de los conflictos intraestatales son de vital importancia para los responsables políticos y las organizaciones no gubernamentales (ONG) internacionales. Utilizando un enfoque de datos de recuento, proponemos un modelo de regresión Hurdle jerárquico para abordar el correspondiente reto de predicción a nivel mensual de PRIO-GRID. Más concretamente, modelamos la intensidad del conflicto armado local en un momento determinado como un proceso de tres etapas. Las etapas uno y dos de nuestro enfoque estiman si observaremos alguna víctima a nivel de país y de celda de la red, respectivamente, mientras que la etapa tres aplica un modelo de regresión para datos truncados con el propósito de predecir la cantidad potencial de dichas víctimas mortales en función de las dos etapas anteriores. Dentro de este marco de modelización, nos centramos en el rol de las importaciones de armas por parte de los gobiernos como un factor de proceso que permite a los gobiernos intensificar o impedir los enfrentamientos. Además, sostenemos que la lejanía geográfica de una célula de la red está destinada a moderar los efectos de estas concentraciones militares. Las predicciones fuera de la muestra corroboran la eficacia de nuestro modelo parsimonioso y basado en la teoría, que permite una transparencia total combinada con precisión en el proceso de previsión. Les modèles de prévision précis et interprétables, qui permettent de prédire spatialement et temporellement les détails des changements dans les nombres de victimes de conflits intra-étatiques, sont d'une importance cruciale pour les décideurs politiques et les organizations non gouvernementales (ONG) internationales. Nous adoptons une approche par données de comptage et nous proposons un modèle de régression hiérarchique à obstacle (hurdle) pour relever le défi de la prédiction correspondante au niveau de la grille mensuelle du PRIO (Peace research institute Oslo, Institut de recherche sur la paix d'Oslo). Plus précisément, nous modélisons l'intensité des conflits armés locaux à un moment spécifique sous la forme d'un processus en trois étapes. Les étapes un et deux de notre approche consistent à estimer si nous observerons des pertes respectivement au niveau du pays et de la cellule de grille, tandis que l'étape trois consiste à appliquer un modèle de régression pour les données tronquées afin de prédire le nombre de ces pertes en fonction des deux étapes précédentes. Dans ce cadre de modélisation, nous nous concentrons sur le rôle des importations d'armes gouvernementales en tant que facteur processuel permettant aux gouvernements d'intensifier ou de dissuader les combats. Nous soutenons également que l'isolement géographique d'une cellule de la grille est susceptible de modérer les effets de ces renforcements militaires. Des prédictions hors échantillon corroborent l'efficacité de notre modèle parcimonieux fondé sur la théorie qui permet une totale transparence associée à une précision du processus de prévision. [ABSTRACT FROM AUTHOR]

It is not unusual to have outliers and multicollinearity simultaneously in censored semiparametric linear models. In this paper for dealing with multicollinearity and outliers we introduce a family of robust censored Liu and non-Liu type of estimates for the regression parameter when some non-stochastic linear restrictions are imposed. The proposed robust estimators is based on least trimmed squares (LTS) method. The efficiency of LTS estimator statistically is more than the well-known least median squares (LMS) estimator. Unfortunately the computational complexity of LTS is less well understood than that of LMS in which the objective is to minimize the median squared residual. Here, we provide the robust estimators for linear and non-linear parts of the censored model based on robust shrinkage Liu estimators. The performance of proposed estimators compared to ordinary estimators numerically is evaluated by Monte Carlo simulation studies. We further illustrate the our procedures by an application. [ABSTRACT FROM AUTHOR]

Statistical learning is evolving quickly, with increasingly sophisticated models seeking to incorporate the complicated data structures from modern scientific and business problems. Varying-index coefficient models extend varying-coefficient models and single-index models for semiparametric regressions. This new class of model offers greater exibility in terms of characterizing complicated nonlinear interaction effcts in a regression analysis. To safeguard against outliers and extreme observations, we consider a robust quantile regression approach to estimate the model parameters. High-dimensional loading parameters are allowed in our development, under reasonable theoretical conditions. Thus, we propose a regularized estimation procedure to select the significant nonzero loading parameters, identify linear functions in varying-index coefficient models, and consistently estimate the parametric and nonparametric components. Under some technical assumptions, we show that the proposed procedure is consistent in terms of variable selection and linear function identification, and that the proposed parameter estimation enjoys the oracle property. Extensive simulation studies are carried out to assess the finite-sample performance of the proposed method. We illustrate our methods using an example based on New Zealand workforce data. [ABSTRACT FROM AUTHOR]

Effect modification occurs when the effect of the treatment on an outcome varies according to the level of other covariates and often has important implications in decision‐making. When there are tens or hundreds of covariates, it becomes necessary to use the observed data to select a simpler model for effect modification and then make valid statistical inference. We propose a two‐stage procedure to solve this problem. First, we use Robinson's transformation to decouple the nuisance parameters from the treatment effect of interest and use machine learning algorithms to estimate the nuisance parameters. Next, after plugging in the estimates of the nuisance parameters, we use the lasso to choose a low‐complexity model for effect modification. Compared to a full model consisting of all the covariates, the selected model is much more interpretable. Compared to the univariate subgroup analyses, the selected model greatly reduces the number of false discoveries. We show that the conditional selective inference for the selected model is asymptotically valid given the rate assumptions in classical semiparametric regression. Extensive simulation studies are conducted to verify the asymptotic results and an epidemiological application is used to demonstrate the method. [ABSTRACT FROM AUTHOR]