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This paper established a novel approach for developing simplified yet accurate models using artificial neural networks (ANNs) in industrial environments. It demonstrates that combining nonlinear regression with neural network modeling enhances predictive accuracy while maintaining the inherent simplicity of ANNs. Industrial sectors are increasingly adopting environmentally friendly practices, driven by the recognition that sustainable initiatives can lead to significant and lasting financial benefits rather than merely a sense of ecological duty. Integrating energy efficiency practices offers potential advantages in waste reduction and resource conservation, which can decrease operating expenses over time. This contributes significantly to pollution mitigation by reducing overall energy consumption cost‐effectively. Numerical simulations based on experimental results validate the proposed method, addressing the complexity and accuracy challenges in business models within the energy sector. [ABSTRACT FROM AUTHOR]

The true prediction of food crop with exact estimation of population growth can play a crucial role for evolving effective agricultural policy decisions for food concerns. This study integrated the efficacies of supervised machine learning (ML) algorithms using compound growth exponential regression models (CGREM) with linear regression models (LRM) to predict the wheat area, yield and population explosion. The historical data are collected from 1950 to 2020 and ML models are deployed using the 80% train and 20% test datasets. Various combinations of train test split have been applied to check the precisions of data partitions subset on the deployed models. ML model predict that the wheat area will rise up to 15%, 51.7%, wheat yield will grow up to 28%, 109.7% and population will rise up to 34.2%, 140.6%, respectively for the year 2030 and 2050. Population will upturn about 88.9% and 30.9% more than from wheat area and wheat yield up to 2050 and it might explode the food critiques in the region. It is concluded that wheat productivity must be raise to ensure the food demands. The results of this study demonstrated that the CGREM found to be superior comparing with benchmark LRM. [ABSTRACT FROM AUTHOR]

The true prediction of food crop with exact estimation of population growth can play a crucial role for evolving effective agricultural policy decisions for food concerns. This study integrated the efficacies of supervised machine learning (ML) algorithms using compound growth exponential regression models (CGREM) with linear regression models (LRM) to predict the wheat area, yield and population explosion. The historical data are collected from 1950 to 2020 and ML models are deployed using the 80% train and 20% test datasets. Various combinations of train test split have been applied to check the precisions of data partitions subset on the deployed models. ML model predict that the wheat area will rise up to 15%, 51.7%, wheat yield will grow up to 28%, 109.7% and population will rise up to 34.2%, 140.6%, respectively for the year 2030 and 2050. Population will upturn about 88.9% and 30.9% more than from wheat area and wheat yield up to 2050 and it might explode the food critiques in the region. It is concluded that wheat productivity must be raise to ensure the food demands. The results of this study demonstrated that the CGREM found to be superior comparing with benchmark LRM. [ABSTRACT FROM AUTHOR]

Prediction models of coronavirus disease utilizing machine learning algorithms range from forecasting future suspect cases, predicting mortality rates, to building a pattern for country-specific pandemic end date. To predict the future suspect infection and death cases, we categorized the approaches found in the literature into: first, a purely data-driven approach, whose goal is to build a mathematical model that relates the data variables including outputs with inputs to detect general patterns. The discovered patterns can then be used to predict the future infected cases without any expert input. The second approach is partially data-driven; it uses historical data, but allows expert input such as the SIR epidemic algorithm. This approach assumes that the epidemic will end according to medical reasoning. In this paper, we compare the purely data-driven and partially-data driven approaches by applying them to data from three countries having different past pattern behavior. The countries are the US, Jordan, and Italy. It is found that those two prediction approaches yield significantly different results. Purely data-driven approach depends totally on the past behavior and does not show any decline in the number of the infected cases if the country did not experience any decline in the number of cases. On the other hand, a partially data-driven approach guarantees a timely decline of the infected curve to reach zero. Using the two approaches highlights the importance of human intervention in pandemic prediction to guide the learning process as opposed to the purely data-driven approach that predicts future cases based on the pattern detected in the data. [ABSTRACT FROM AUTHOR]

In this article, we introduce the commands twexp and twgravity, which implement the estimators developed in Jochmans (2017, Review of Economics and Statistics 99: 478–485) for exponential regression models with two-way fixed effects. twexp is applicable to generic n × m panel data. twgravity is written for the special case where the dataset is a cross-section on dyadic interactions between n agents. A prime example is cross-sectional bilateral trade data, where the model of interest is a gravity equation with importer and exporter effects. Both twexp and twgravity can deal with data where n and m are large, that is, where there are many fixed effects. These commands use Mata and are fast to execute. [ABSTRACT FROM AUTHOR]

By estimating a flexible nonlinear regression model of savings on an original dataset of service procurements conducted by the Spanish Armed Forces, this paper provides robust and precise novel econometric evidence on the extent and sources of cost savings in public procurement. The net effect on savings of the policy-amenable and economically advantageous variables that we estimate, such as the size of the procured function, the importance of price in the contract award criteria, and the number of bidders who participate in the tendering, may help contracting agencies to select management practices and to forecast the price paid out. We find that savings increase proportionally to the size of the procured function, that an increase of 10 percentage points in the importance of price increases savings by approximately 2% of the function's size, and that savings are generally reduced by restricting the number of bidders. A comparison with estimates reported in previous studies is also made. [ABSTRACT FROM AUTHOR]

In this article, we suggest simple moment-based estimators to deal with unobserved heterogeneity in a special class of nonlinear regression models that includes as main particular cases exponential models for nonnegative responses and logit and complementary loglog models for fractional responses. The proposed estimators: (i) treat observed and omitted covariates in a similar manner; (ii) can deal with boundary outcomes; (iii) accommodate endogenous explanatory variables without requiring knowledge on the reduced form model, although such information may be easily incorporated in the estimation process; (iv) do not require distributional assumptions on the unobservables, a conditional mean assumption being enough for consistent estimation of the structural parameters; and (v) under the additional assumption that the dependence between observables and unobservables is restricted to the conditional mean, produce consistent estimators of partial effects conditional only on observables. [ABSTRACT FROM AUTHOR]

In this article, we discuss the optimal allocation problem in a multi-level accelerated life-testing experiment under Type-I censoring when an exponential regression model is used for the analysis. We derive the expected Fisher information matrix and use it to obtain the asymptotic variance–covariance matrix of the maximum likelihood estimators (MLEs). We then consider the optimal allocation under theD-optimality criterion, and present an algorithm for determining the optimal allocation. A numerical example is presented for the purpose of illustration. The optimal allocation depends on the model parameters and so a sensitivity analysis of the optimal allocation to misspecification of the model parameters is carried out as well. [ABSTRACT FROM PUBLISHER]

We propose a penalized likelihood method to detect the possible outliers in the exponential regression model while it is utilized to fit the censored survival data. It features that the proposed method can simultaneously cope with outlier detection and estimation for the regression coefficient. We recast the outlier detection issue into a high-dimensional regularization regression and employ the coordinate descent algorithm to facilitate the computation. From both extensive simulation studies and an illustrative real example, it is shown that the proposed method works quite well in outlier detection as well as parameter estimation for the exponential regression model. [ABSTRACT FROM PUBLISHER]

Mechanical devices are playing a crucial role in modern industry. With the ever-growing demands of multiple function and high performance, the unpredicted failures of mechanical device might greatly increase maintenance cost during its lifetime. As a key state indicator of mechanical device, the degradation of some important performance provides substantial information for failure prognosis. More and more attention has been paid to the degradation-based failure time prediction. However, even mechanical devices of the same type might show greatly diverse degradation processes under different working environments. It is still a challenge to identify global degradation pattern and then predict the failure time of a specific mechanical device based on its degradation sequence. This paper proposes a novel approach for failure time prediction with the degradation sequence of mechanical device. The proposed approach combines the exponential regression and parametric empirical Bayesian (PEB) technology. Firstly, exponential regression is adopted to represent the local degradation pattern and then local failure time observations can be computed. Secondly, according to the rule that local failure time observations manifest, appropriate prior assumption is made and the posterior distribution is estimated by PEB technology. Herein, two prior assumptions are considered, including the exchangeable PEB and linear PEB case. The global failure time distribution can be predicted with the estimated prior and posterior distribution. Finally, three case studies are implemented to validate the proposed approach, including the simulation case, crack case and precision case of machine tool. [ABSTRACT FROM AUTHOR]

We study residual plots for parametric accelerated failure time (AFT) models, using both standardized residuals and Cox-Snell residuals. Two different approaches are discussed in the case of censored data; adjusting censored residuals by adding a residual time, and using nonparametric exponential regression of non-adjusted censored Cox-Snell residuals. The main object of the paper is to show how residuals can be used to infer the correct functional form for possibly misspecified covariates. We demonstrate the use of the methods by analysis of two reliability data sets and by a simulation study using Weibull-distributed data. We also consider briefly a corresponding approach for parametric proportional hazards models. [ABSTRACT FROM AUTHOR]

The idea of sensorless position sensing of switched reluctance motor (SRM) is attractive to researchers because of the increased reliability, robustness, and cost reduction compared to conventional drives. Sensorless drive is particularly useful in electric transportation applications where the environment is too hostile for physical position sensors, such as inside an electric car or bus. This paper presents a new method to estimate the motor positions during startup or at flying restart. Unlike most of the methods described in the literature, the algorithm, based only on the general magnetic characteristics of an SRM, can provide exact rotor positions without specific motor magnetic information. The calculation is simple and can be implemented easily and efficiently with a microcontroller by users in industry. [ABSTRACT FROM PUBLISHER]

Purpose: Our aim was to compare fit and predictive performance effectiveness of four pointwise regression models in measuring the visual field (VF) decay rate of progression in patients with open-angle glaucoma. Methods: We selected Humphrey VF data of patients with open-angle glaucoma with a minimum follow-up time of 6 years. For each eye ( n = 798 from 588 patients), we regressed threshold sensitivity ( y) at each VF test location for the entire VF series against follow-up time ( x), with four candidate first-order regression models: (1) ordinary least-squares linear regression model ( y = β + β x); (2) nondecay exponential regression model ( y = β + βe); (3) decay exponential regression model ( $$ y \,=\, {\text{e}}^{{\beta_{0} + \beta_{1} x}} $$ ); (4) Tobit-censored, maximum-likelihood linear regression model ( y* = $$ \beta 0 + \beta 1{\text{x}} + \varepsilon $$ , ε ~ N(0, σ)), where x is follow-up time and y is threshold sensitivity. Results: The average [± standard deviation (SD)] baseline VF mean deviation (MD) was −8.2 (±5.5) dB, the mean follow-up was 8.7 (±1.9) years, and the number of follow-up VFs was 14.7 (±4.4). The decay exponential model was the best-fitting (42.7 % of locations) and best-forecasting (65.5 % of locations) model. The decay exponential model was the best prediction model in all categories of severity. Conclusions: It is not clear that the ordinary least-squares linear regression model is always the favored model for fitting and forecasting VF data in patients with glaucoma. The pointwise decay exponential regression (PER) model was the best-fitting and best-predicting model across a wide range of glaucoma severity and can be readily understood by clinicians. [ABSTRACT FROM AUTHOR]

Hedonic methods are a prominent approach in the construction of quality-adjusted price indexes. This paper shows that the process of computing such indexes is substantially simplified if arithmetic (geometric) price indexes are computed based on exponential (log-linear) hedonic functions estimated by the Poisson pseudo-maximum likelihood (ordinary least squares) method. A Monte Carlo simulation study based on housing data illustrates the convenience of the links identified and the very attractive properties of the Poisson estimator in the hedonic framework. [ABSTRACT FROM AUTHOR]

Three experiments were conducted to determine and validate the relative bioefficacy of the liquid form of hydroxy analog (liquid MHA-FA) to that of DL-methionine (Met) in male broilers. In experiments 1 and 2, 945 and 550 male broilers were fed either a Met-deficient basal diet, or the basal diet supplemented with four and five equimolar levels of each Met source, respectively. In experiment 3, 1232 male broilers were fed either a Met-deficient basal diet, or the basal diet with three levels (low, moderate, or high) of liquid MHA-FA or DL-Met at 65% of the liquid MHA-FA level. Growth performance improved in all trials, regardless of the Met source, relative to those broilers fed the basal diet. In experiments 1 and 2, the bioefficacy estimates for liquid MHA-FA relative to DL-Met on a product basis were: 50% and 64% for weight gain, 51% and 59% for the feed conversion ratio, and 54% and 48% for breast-meat yield, respectively. In experiment 3, there were no differences between the DL-Met or liquid MHA-FA treatments, and the broilers fed liquid MHA-FA were 68% as efficient on a product basis as those fed DL-methionine for weight gain. The results from these three trials indicate that the bioefficacy of liquid MHA-FA relative to DL-methionine is 57% on a product basis on average across all criteria tested. [ABSTRACT FROM AUTHOR]

Simple Summary: Rates of viral spread during first and second waves of the COVID-19 pandemic for USA states, and for consecutive nonoverlapping periods of 20 days for the USA and 51 countries across the globe associate with mean temperature, elevation, population density and age. Some associations switch directions when comparing different periods. Even population density, which presumably should always increase viral spread, at some periods seems to decrease spread rates. We also observed systematic inversions between spread rates estimated at 80–100 day intervals. These patterns remain unexplained and suggest difficulties in managing and predicting the pandemic, in particular, negative correlations between population density and spread rates, which were observed in independent samples and at different periods. Putatively, confinements could produce these patterns, by selecting viral strains with longer contagiousness and/or latent periods. We present spread parameters for first and second waves of the COVID-19 pandemic for USA states, and for consecutive nonoverlapping periods of 20 days for the USA and 51 countries across the globe. We studied spread rates in the USA states and 51 countries, and analyzed associations between spread rates at different periods, and with temperature, elevation, population density and age. USA first/second wave spread rates increase/decrease with population density, and are uncorrelated with temperature and median population age. Spread rates are systematically inversely proportional to those estimated 80–100 days later. Ascending/descending phases of the same wave only partially explain this. Directions of correlations with factors such as temperature and median age flip. Changes in environmental trends of the COVID-19 pandemic remain unpredictable; predictions based on classical epidemiological knowledge are highly uncertain. Negative associations between population density and spread rates, observed in independent samples and at different periods, are most surprising. We suggest that systematic negative associations between spread rates 80–100 days apart could result from confinements selecting for greater contagiousness, a potential double-edged sword effect of confinements. [ABSTRACT FROM AUTHOR]

We applied the grey system theory to evaluation of chlorophyll (Chl) degradation in Chamaecyparis Sieb. & Zucc. var. formosana (Hayata) Rehder needle-leaf in the Yuanyang Lake Nature Preserve of northern Taiwan. Pigment analysis was finished within 12 h after collecting the samples. Four grey prediction models for the degradation of Chl a, Chl b, and for the change of Chl a/b ratio and water content were established and compared with the results of linear and exponential regression analysis. The residual error and accuracy range show that the grey prediction process is much better than regression analysis. The degradation of Chl a and b contains two phases, one being fast and the other slow. [ABSTRACT FROM AUTHOR]

Components in electronic systems are often observed to be likely to fail at an early age, an effect well known as the 'infant mortality' effect. Similarly, for systems composed of such components, a decrease in the rate of occurrence of failures, usually called the 'reliability improvement' effect, is seen in the early operating period. In this paper we show that such improvement may be considered as a simple consequence of the heterogeneity among the components in the population. We present a simple three-parameter model for the distribution of the component lifetime, which has a simple physical interpretation, and from which we obtain methods for statistical inference, suitable for implementation on even small computers. The methods have been applied successfully to field failure data, collected from an industrial company. [ABSTRACT FROM AUTHOR]

In the exponential regression model, Bayesian inference concerning the non-linear regression parameter σ has proved extremely difficult. In particular, standard improper diffuse priors for the usual parameters lead to an improper posterior for the non-linear regression parameter. In a recent paper Ye and Berger (1991) applied the reference prior approach of Bernardo (1979) and Berger and Bernardo (1989) yielding a proper informative prior for σ. This prior depends on the values of the explanatory variable, goes to 0 as σ goes to 1, and depends on the specification of a hierarchical ordering of importance of the parameters. This paper explains the failure of the uniform prior to give a proper posterior: the reason is the appearance of the determinant of the information matrix in the posterior density for σ. We apply the posterior Bayes factor approach of Aitkin (1991) to this problem; in this approach we integrate out nuisance parameters with respect to their conditional posterior density given the parameter of interest. The resulting integrated likelihood for σ requires only the standard diffuse prior for all the parameters, and is unaffected by orderings of importance of the parameters. Computation of the likelihood for σ is extremely simple. The approach is applied to the three examples discussed by Berger and Ye and the likelihoods compared with their posterior densities. [ABSTRACT FROM AUTHOR]

This article deals with specifications of informative prior distributions for generalized linear models. Our emphasis is on specifying distributions for selected points on the regression surface, the prior distribution on regression coefficients is reduced from this specification. We believe that it is inherently easier to think about conditional means of observables given the regression variables than it is to think about model-dependent regression coefficients. Previous use of conditional means priors seems to be restricted to logistic regression with one predictor variable and to normal theory regression. We expand on the idea of conditional means priors and extend these to arbitrary generalized linear models. We also consider data augmentation priors where the prior is of the same form as the likelihood. We show that data augmentation priors are special cases of conditional means priors. With current Monte Carlo methodology, such as importance sampling and Gibbs sampling, our priors result in tractable posteriors. [ABSTRACT FROM AUTHOR]

The rapid, accurate, and economical estimation of crop above-ground biomass at the farm scale is crucial for precision agricultural management. The unmanned aerial vehicle (UAV) remote-sensing system has a great application potential with the ability to obtain remote-sensing imagery with high temporal-spatial resolution. To verify the application potential of consumer-grade UAV RGB imagery in estimating maize above-ground biomass, vegetation indices and plant height derived from UAV RGB imagery were adopted. To obtain a more accurate observation, plant height was directly derived from UAV RGB point clouds. To search the optimal estimation method, the estimation performances of the models based on vegetation indices alone, based on plant height alone, and based on both vegetation indices and plant height were compared. The results showed that plant height directly derived from UAV RGB point clouds had a high correlation with ground-truth data with an R2 value of 0.90 and an RMSE value of 0.12 m. The above-ground biomass exponential regression models based on plant height alone had higher correlations for both fresh and dry above-ground biomass with R2 values of 0.77 and 0.76, respectively, compared to the linear regression model (both R2 values were 0.59). The vegetation indices derived from UAV RGB imagery had great potential to estimate maize above-ground biomass with R2 values ranging from 0.63 to 0.73. When estimating the above-ground biomass of maize by using multivariable linear regression based on vegetation indices, a higher correlation was obtained with an R2 value of 0.82. There was no significant improvement of the estimation performance when plant height derived from UAV RGB imagery was added into the multivariable linear regression model based on vegetation indices. When estimating crop above-ground biomass based on UAV RGB remote-sensing system alone, looking for optimized vegetation indices and establishing estimation models with high performance based on advanced algorithms (e.g., machine learning technology) may be a better way. [ABSTRACT FROM AUTHOR]

New fixed-effects estimators are proposed for logit and complementary loglog fractional regression models. The standard specifications of these models are transformed into a form of exponential regression with multiplicative individual effects and time-variant heterogeneity, from which four alternative estimators that do not require assumptions on the distribution of the unobservables are proposed. All new estimators are robust to both time-variant and time-invariant heterogeneity and can accommodate fractional responses with observations at the boundary value of zero. Additionally, some of these estimators can be applied to dynamic panel data models and can accommodate endogenous explanatory variables without requiring the specification of a reduced form model. A Monte Carlo study and an application to firm capital structure choices illustrate the usefulness of the suggested estimators. [ABSTRACT FROM AUTHOR]