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In this paper, we study the robust estimation and empirical likelihood for the regression parameter in generalized linear models with right censored data. A robust estimating equation is proposed to estimate the regression parameter, and the resulting estimator has consistent and asymptotic normality. A bias-corrected empirical log-likelihood ratio statistic of the regression parameter is constructed, and it is shown that the statistic converges weakly to a standard $ \chi ^2 $ χ 2 distribution. The result can be directly used to construct the confidence region of regression parameter. We use the bias correction method to directly calibrate the empirical log-likelihood ratio, which does not need to be multiplied by an adjustment factor. We also propose a method for selecting the tuning parameters in the loss function. Simulation studies show that the estimator of the regression parameter is robust and the bias-corrected empirical likelihood is better than the normal approximation method. An example of a real dataset from Alzheimer's disease studies shows that the proposed method can be applied in practical problems. [ABSTRACT FROM AUTHOR]

AbstractThe restricted mean survival time (RMST) emerges as a crucial summary metric in survival analysis. In some cases, certain covariates may have a varying impact on RMST at one specific change-point. For instance, consider studies focusing on women’s health, where a notable shift in a patient’s lifespan might occur when the age at menopause surpasses a particular cutoff value. To tackle this complex phenomenon, we consider a regression analysis of RMST with change-point in the covariate effects. This involves building a generalized linear model that directly captures the relationship between RMST and associated covariates that may have a change-point. Using the smoothed estimating equation with pseudo-observations, we obtain parameter estimators and establish the large sample properties. Finally, we assess the finite sample performance of the proposed method through simulation studies and apply it to real data examples from primary biliary cirrhosis and rectal cancer studies. [ABSTRACT FROM AUTHOR]

Emerging economies and ecosystems are critically dependent on fossil fuels, and a country's energy dependence is a significant measure of its reliance on foreign suppliers. This study evaluates the impact of energy reliance on energy intensity, CO2 emission intensity, and the utilization of renewable resources in 35 developing and 20 developed nations, as well as the connection between renewable energy (REN), GDP growth, and CO2 emissions. This study employs the generalized linear model (GLM) and the robust least squares (RLS) method to assess the inverse association between renewable energy and developed and developing economy policymakers, utilizing unique linear panel estimate approaches (1970--2022). The impact of renewable energy as a response variable on economic growth, energy consumption, and CO2 emissions across four continents is investigated in this study. The findings indicate that developing countries experience a rise in per capita CO2 emissions if their renewable energy use exceeds their capacity. This finding remains significant even when other proxies for renewable energy use are introduced using modified approaches. Furthermore, it is particularly relevant to industrialized nations that possess more developed institutions. Even more surprisingly, in terms of the energy and emission intensity required for growth, energy dependence has accelerated all components. The regional analysis revealed a spillover impact in most areas, suggesting that the consequences of energy dependence are essentially the same in neighboring countries. The growth of the renewable energy sector and the decrease in greenhouse gas emissions depend critically on the ability of regional energy exchange unions to mitigate the negative environmental and economic impacts of energy dependency. These underdeveloped countries need to spend more on research and development to catch up technologically. [ABSTRACT FROM AUTHOR]

Introduction: African swine fever (ASF) is a disease with a high mortality rate and high transmissibility. Identifying high-risk clusters and understanding the transmission characteristics of ASF in advance are essential for preventing its spread in a short period of time. This study investigated the spatial and temporal heterogeneity of ASF in the Republic of Korea by analyzing surveillance data on wild boar carcasses. Methods: We observed a distinct annual propagation pattern, with the occurrence of ASF-infected carcasses trending southward over time. We developed a rankbased statistical model to evaluate risk by estimating the average weekly number of carcasses per district over time, allowing us to analyze and identify risk clusters of ASF. We conducted an analysis to identify risk clusters for two distinct periods, Late 2022 and Early 2023, utilizing data from ASF-infected carcasses. To address the underestimation of risk and observation error due to incomplete surveillance data, we estimated the number of ASF-infected individuals and accounted for observation error via different surveillance intensities. Results: As a result, in Late 2022, the risk clusters identified by observed and estimated number of ASF-infected carcasses were almost identical, particularly in the northwestern Gyeongbuk region, north Chungbuk region, and southwestern Gangwon region. In Early 2023, we observed a similar pattern with numerous risk clusters identified in the same regions as in Late 2022. Discussion: This approach enhances our understanding of ASF spatial dynamics. Additionally, it contributes to the epidemiology and study of animal infectious diseases by highlighting areas requiring urgent and focused intervention. By providing crucial data for the targeted allocation of resources for disease management and preventive measures, our findings lay vital groundwork for improving ASF management strategies, ultimately aiding in the containment and control of this devastating disease. [ABSTRACT FROM AUTHOR]

Simple Summary: Spatial patterns are characteristics of spatial processes, although they do not always match each other exactly. An ordinary way to explore the spatial progresses driving these spatial patterns is spatial pattern analysis, which has been widely employed in ecology studies but not in the studies of forest diseases. For diseased forest trees, the spatial pattern is a complex of the spatial pattern of forest trees and the disease itself. So, it is necessary to explore whether an antecedent pattern of host/nonhost trees affects the spatial pattern of a forest disease. Another subject that is neglected is the effect size of an antecedent pattern. In this study, taking a stem fungal disease caused by Inonotus hispidus as an example, we explored the two questions. Our results showed that the spatial pattern of host size affected the spatial pattern of the infection and the infection likelihood of the focal tree. Our results provide a new perspective to understand the effect of host patterns on forest disease. The spatial pattern of diseased forest trees is a product of the spatial pattern of host trees and the disease itself. Previous studies have focused on describing the spatial pattern of diseased host trees, and it remains largely unknown whether an antecedent spatial pattern of host/nonhost trees affects the infection pattern of a disease and how large the effect sizes of the spatial pattern of host/nonhost trees and host size are. The results from trivariate random labeling showed that the antecedent pattern of the host ash tree, Fraxinus mandshurica, but not of nonhost tree species, impacted the infection pattern of a stem fungal disease caused by Inonotus hispidus. To investigate the effect size of the spatial pattern of ash trees, we employed the SADIE (Spatial Analysis by Distance IndicEs) aggregation index and clustering index as predictors in the GLMs. Globally, the spatial pattern (vi index) of ash trees did not affect the infection likelihood of the focal tree; however, the spatial pattern of DBH (diameter at breast height) of ash trees significantly affected the infection likelihood of the focal tree. We sampled a series of circular plots with different radii to investigate the spatial pattern effect of host size on the infection likelihood of the focal tree locally. The results showed that the location (patch/gap) of the DBH of the focal tree, rather than that of the focal tree itself, significantly affected its infection likelihood in most plots of the investigated sizes. A meta-analysis was employed to settle the discrepancy between plots of different sizes, which led to results consistent with those of global studies. The results from meta-regression showed that plot size had no significant effects. [ABSTRACT FROM AUTHOR]

Matrix-variate generalized linear model (mvGLM) has been investigated successfully under the framework of tensor generalized linear model, because matrix-form data can be regarded as a specific tensor (2-dimension). But there are few works focusing on matrix-form data with measurement error (ME), since tensor in conjunction with ME is relatively complex in structure. In this paper we introduce a mvGLM to primarily explore the influence of ME in the model with matrix-form data. We calculate the asymptotic bias based on error-prone mvGLM, and then develop bias-correction methods to tackle the affect of ME. Statistical properties for all methods are established, and the practical performance of all methods is further evaluated in analysis on synthetic and real data sets. [ABSTRACT FROM AUTHOR]

The quality of generalized linear models (GLMs), frequently used by insurance companies, depends on the choice of interacting variables. The search for interactions is time-consuming, especially for data sets with a large number of variables, depends much on expert judgement of actuaries, and often relies on visual performance indicators. Therefore, we present an approach to automating the process of finding interactions that should be added to GLMs to improve their predictive power. Our approach relies on neural networks and a model-specific interaction detection method, which is computationally faster than the traditionally used methods like Friedman's H-Statistic or SHAP values. In numerical studies, we provide the results of our approach on artificially generated data as well as open-source data. [ABSTRACT FROM AUTHOR]

The impact of heatwaves (HWs) on human health is a topic of growing interest due to the global magnification of these phenomena and their substantial socio‐economic impacts. As for other countries of Southern Europe, Spain is a region highly affected by heat and its increase under climate change. This is observed in the mean values and the increasing incidence of extreme weather events and associated mortality. Despite the vast knowledge on this topic, it remains unclear whether specific types and characteristics of HW are particularly harmful to the population and whether this shows a regional interdependency. The present study provides a comprehensive analysis of the relationship between HW characteristics and mortality in 12 Spanish cities. We used separated time series analysis in each city applying a quasi‐Poisson regression model and distributed lag linear and non‐linear models. Results show an increase in the mortality risk under HW conditions in the cities with a lower HW frequency. However, this increase exhibits remarkable differences across the cities under study not showing any general pattern in the HW characteristics‐mortality association. This relationship is shown to be complex and strongly dependent on the local properties of each city pointing out the crucial need to examine and understand on a local scale the HW characteristics and the HW‐mortality relationship for an efficient design and implementation of prevention measures. Plain Language Summary: Heatwaves (HWs) are episodes of extreme heat sustained in time with devastating socio‐economic impacts. Due to their global magnification, the interest in their impacts on human health has increased. Spain, in Southern Europe, is a climate change hot spot, particularly in relation to increasing temperature extremes. Despite the relevance of the topic, it is still unclear if there are particular characteristics of heatwaves with a larger impact on mortality. In the present study, we analyze the relationship between heatwaves' characteristics and mortality risk in 12 Spanish cities. Results show no general pattern for the relationship between mortality and heatwave characteristics over the 12 cities under study, but local relations point out the need for local studies to accurately assess the relationship between heatwave characteristics and mortality for an efficient implementation of prevention measures. Key Points: No general patterns found describing the heatwave characteristics‐mortality associationThe heatwave characteristics‐mortality coupling needs a local scale analysis due to its complex and strong dependence on local propertiesThe health indices recovery factor and excess heat factor do not always represent the heatwave‐mortality association [ABSTRACT FROM AUTHOR]

The generalized linear model approach of the mixed Poisson regression models (MPRM) is suitable for over-dispersed count data. The maximum likelihood estimator (MLE) is adopted to estimate their regression coefficients. However, the variance of the MLE becomes high when the covariates are collinear. The Poisson-Modification of Quasi Lindley (PMQL) regression model is a recently introduced model as an alternative MPRM. The variance of the proposed MLE for the PMQL regression model is high in the presence of multicollinearity. This paper adopts the ridge regression method for the PMQL regression model to combat such an issue, and we use several notable methods to estimate its ridge parameter. A Monte Carlo simulation study was designed to evaluate the performance of the MLE and the different PMQL ridge regression estimators by using their scalar mean square (SMSE) values. Further, we analyzed a simulated data and a real-life applications to show the consistency of the simulation results. The simulation and applications results indicate that the PMQL ridge regression estimators dominate the MLE when multicollinearity exists. [ABSTRACT FROM AUTHOR]

Resistance traits are economically important in crops in terms of accessibility to promising resistant germplasm. This study was conducted to evaluate SNP marker-trait association for Cereal Cyst Nematode (CCN), Heterodera filipjevi, in a large number of natural bread wheat populations. Phenotypic data analyzed using GLM (Generalized Linear Model) indicated significant differences among the landrace accessions for resistance to H. filipjevi. The genotyping was performed by 152K SNP chip on 188 accessions. After filtering, 10,471 polymorphic SNPs were employed for Genome Wide Association Study (GWAS). Population structure among the wheat genotypes were investigated using 840 well distinct SNP markers. Two sub-populations were revealed by structure software, and eleven markers were found to be significantly (P-value< 0.001) associated with resistance to H. filipjevi on chromosomes 2A, 3B, 4A, 4B, 5A, 5B, 5D, and 6B. The linkage disequilibrium analysis for all significantly associated SNPs showed that markers on chromosomes 4A and 4B were in high intra-chromosomal linkage disequilibrium, and, consequently, eight markers were recommended as strongly associated with resistance to H. filipjevi. The present study demonstrated valuable sources of resistance in the studied wheat genotypes against a widespread and important species of CCNs. The associated markers could be used in molecular breeding programs of bread wheat. [ABSTRACT FROM AUTHOR]

This article is concerned with the optimal design problem of efficient statistical inference for comparing regression mean curves in two generalized linear models (GLMs) estimated from two samples of independent measurements. The main objective is to find the locally μ p -optimal designs for given values of the model parameters that minimize an L p -norm of the asymptotic variance of the difference between the two estimated regression curves. Two equivalence theorems are given to verify the locally μ p -optimality of the designs in the set of all approximate designs for the comparison of regression curves in two GLMs. Several numerical examples are presented to illustrate the superiorities of the locally μ p -optimal designs ( p = 1 , ∞ ) by comparing them with equidistant designs and individual D-optimal designs for the comparison of regression curves in different scenarios. [ABSTRACT FROM AUTHOR]

Response functions that link regression predictors to properties of the response distribution are fundamental components in many statistical models. However, the choice of these functions is typically based on the domain of the modeled quantities and is usually not further scrutinized. For example, the exponential response function is often assumed for parameters restricted to be positive, although it implies a multiplicative model, which is not necessarily desirable or adequate. Consequently, applied researchers might face misleading results when relying on such defaults. For parameters restricted to be positive, we propose to construct alternative response functions based on the softplus function. These response functions are differentiable and correspond closely to the identity function for positive values of the regression predictor implying a quasi-additive model. Consequently, the proposed response functions allow for an additive interpretation of the estimated effects by practitioners and can be a better fit in certain data situations. We study the properties of the newly constructed response functions and demonstrate the applicability in the context of count data regression and Bayesian distributional regression. We contrast our approach to the commonly used exponential response function. [ABSTRACT FROM AUTHOR]

Under a nonlinear regression model with univariate response an algorithm for the generation of sequential adaptive designs is studied. At each stage, the current design is augmented by adding p design points where p is the dimension of the parameter of the model. The augmenting p points are such that, at the current parameter estimate, they constitute the locally D-optimal design within the set of all saturated designs. Two relevant subclasses of nonlinear regression models are focused on, which were considered in previous work of the authors on the adaptive Wynn algorithm: firstly, regression models satisfying the 'saturated identifiability condition' and, secondly, generalized linear models. Adaptive least squares estimators and adaptive maximum likelihood estimators in the algorithm are shown to be strongly consistent and asymptotically normal, under appropriate assumptions. For both model classes, if a condition of 'saturated D-optimality' is satisfied, the almost sure asymptotic D-optimality of the generated design sequence is implied by the strong consistency of the adaptive estimators employed by the algorithm. The condition states that there is a saturated design which is locally D-optimal at the true parameter point (in the class of all designs). [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of designing an experimental plan with both discrete and continuous factors under fairly general parametric statistical models. We propose a new algorithm, named ForLion, to search for locally optimal approximate designs under the D-criterion. The algorithm performs an exhaustive search in a design space with mixed factors while keeping high efficiency and reducing the number of distinct experimental settings. Its optimality is guaranteed by the general equivalence theorem. We present the relevant theoretical results for multinomial logit models (MLM) and generalized linear models (GLM), and demonstrate the superiority of our algorithm over state-of-the-art design algorithms using real-life experiments under MLM and GLM. Our simulation studies show that the ForLion algorithm could reduce the number of experimental settings by 25% or improve the relative efficiency of the designs by 17.5% on average. Our algorithm can help the experimenters reduce the time cost, the usage of experimental devices, and thus the total cost of their experiments while preserving high efficiencies of the designs. [ABSTRACT FROM AUTHOR]

[Objective] This study aimed to clarity the role of sedimentation and erosion in shaping the spatial pattern of soil quality in black soil slope croplands. [Methods] Taking the sloping farmland in typical water-eroded areas in northeast China as the research object, we used the soil attributes of 110 sample points and the soil quality index (SQI) index based on the minimum data set to evaluate the spatial differentiation characteristics of soil quality at the slope scale, while the effects of slope gradient, slope position, and soil depth were determined using generalized linear models (GLMs). [Results] (1) Opposing patterns of soil nutrient content and spatial characteristics were observed between the surface and subsurface layers in sloping croplands. Most nutrient indicators exhibited significantly higher content in the tillage layer compared to the subsurface layer. However, the surface layer showed lower spatial heterogeneity and weaker correlation with related physicochemical indexes comparing with the subsurface layer (p0.05). (3) Soil depth, slope position, and slope gradient emerged as key factors influencing the variability of SQI in slope croplands. The GLM results demonstrated that, for the same soil horizon, slope, aspect, and their interactions explained over 95% variation in SQI. Among them, the explanatory degree of slope position was 68%, and that of slope gradient was 22%. Considering the factor of soil depth, the explanatory degrees of soil depth, slope position, and slope gradient on the variation of SQI in the range of 0—40 cm were 39%, 31%, and 10%, respectively. [Conclusion] The combined method of SQI and GLM was used to clarify the shaping role of the erosion-sedimentation process in the spatial differentiation of black soil quality in sloping cropland, and the research results can provide technical support for the evaluation and management of the quality of eroded and degraded black soil in typical water-eroded areas.

Home automation, supported by smart devices and the internet of things, works to enhance household control. However, the reliance on current systems with fixed rules poses challenges, which can be inflexible and anxiety-provoking for users who want control over their smart home devices, limit responsiveness to changing conditions and affect energy efficiency, comfort and security. To address this, the paper proposes a dynamic personalized recommender system that considers the user's current state and contextual preferences to suggest relevant automation services for smart home devices. The system uses an unsupervised algorithm to extract rules from past interactions and supervised algorithms to make recommendations based on those rules. The proposed context-aware recommender system for smart homes achieved a remarkable average accuracy of 86.99%, a recall of 76.06% and a precision of 82.67% on publicly available datasets, surpassing previous studies. It offers users an enhanced quality of life, energy efficiency and cost reduction, while providing service providers with increased engagement and valuable insights.

This research paper investigated multivariate risk assessment in insurance, focusing on four risks of a singular person and their interdependence. This research examined various risk indicators in non-life insurance which was under-writing for organizations with clients that purchase several non-life insurance policies. The risk indicators are probabilities of frequency claims and correlations of two risk lines. The closed forms of probability mass functions evaluated the probabilities of frequency claims. Three generalized linear models of four-variate Sarmanov distributions were proposed for marginals, incorporating various characteristics of policyholders using explanatory variables. All three models were discrete models that were a combination of Poisson and Gamma distributions. Some properties of four-variate Sarmanov distributions were explicitly shown in closed forms. The dataset spanned a decade and included the exposure of each individual to risk over an extended period. The correlations between the two risk types were evaluated in several statistical ways. The parameters of the three Sarmanov model distributions were estimated using the maximum likelihood method, while the results of the three models were compared with a simpler four-variate negative binomial generalized linear model. The research findings showed that Model 3 was the most accurate of all three models since the AIC and BIC were the lowest. In terms of the correlation, it was found that the risk of claiming auto insurances was related to claiming home insurances. Model 1 could be used for the risk assessment of an insurance company that had customers who held multiple types of insurances in order to predict the risks that may occur in the future. When the insurance company can forecast the risks that may occur in the future, the company will be able to calculate appropriate insurance premiums.

Introduction: Cross frequency coupling (CFC) between electrophysiological signals in the brain is a long-studied phenomenon and its abnormalities have been observed in conditions such as Parkinson's disease and epilepsy. More recently, CFC has been observed in stomach-brain electrophysiologic studies and thus becomes an enticing possible target for diseases involving aberrations of the gut-brain axis. However, current methods of detecting coupling, specifically phase-amplitude coupling (PAC), do not attempt to capture the phase and amplitude statistical relationships. Methods: In this paper, we first demonstrate a method of modeling these joint statistics with a flexible parametric approach, where we model the conditional distribution of amplitude given phase using a gamma distributed generalized linear model (GLM) with a Fourier basis of regressors. We perform model selection with minimum description length (MDL) principle, demonstrate a method for assessing goodness-of-fit (GOF), and showcase the efficacy of this approach in multiple electroencephalography (EEG) datasets. Secondly, we showcase how we can utilize the mutual information, which operates on the joint distribution, as a canonical measure of coupling, as it is non-zero and non-negative if and only if the phase and amplitude are not statistically independent. In addition, we build off of previous work by Martinez-Cancino et al., and Voytek et al., and show that the information density, evaluated using our method along the given sample path, is a promising measure of time-resolved PAC. Results: Using synthetically generated gut-brain coupled signals, we demonstrate that our method outperforms the existing gold-standard methods for detectable low-levels of phase-amplitude coupling through receiver operating characteristic (ROC) curve analysis. To validate our method, we test on invasive EEG recordings by generating comodulograms, and compare our method to the gold standard PAC measure, Modulation Index, demonstrating comparable performance in exploratory analysis. Furthermore, to showcase its use in joint gut-brain electrophysiology data, we generate topoplots of simultaneous high-density EEG and electrgastrography recordings and reproduce seminal work by Richter et al. that demonstrated the existence of gut-brain PAC. Using simulated data, we validate our method for different types of time-varying coupling and then demonstrate its performance to track time-varying PAC in sleep spindle EEG and mismatch negativity (MMN) datasets. Conclusions: Our new measure of PAC using Gamma GLMs and mutual information demonstrates a promising new way to compute PAC values using the full joint distribution on amplitude and phase. Our measure outperforms the most common existing measures of PAC, and show promising results in identifying time varying PAC in electrophysiological datasets. In addition, we provide for using our method with multiple comparisons and show that our measure potentially has more statistical power in electrophysiologic recordings using simultaneous gut-brain datasets. [ABSTRACT FROM AUTHOR]

Budyko-based approaches are widely used to separate climate and catchment effects on streamflow changes. Considering the potential changes in streamflow, such separation is essential for future water resources management. In this study, we propose a method that allows for continuous disaggregation of climate and catchment impacts from the past to the future. We utilize a generalized linear model to represent catchment parameters based on climate and catchment features. Subsequently, we determine the future positions of the catchment in the Budyko-state space. This framework is applied to four Model Parameter Estimation Experiment (MOPEX) catchments with varying aridity indices in the USA. The results indicate that, in the recent historical period (1971–2011), climate change has contributed to increased streamflow in three catchments compared to the baseline (1948–1970) period. However, future projections indicate a mixed pattern of contributions in two of these catchments due to increased potential evapotranspiration, resulting in decreased streamflow. [ABSTRACT FROM AUTHOR]

This paper presents a flexible family of 2- and 3-parameter distributions whose support is the closed interval [0,1], with explicit density, cumulative density, and quantile functions. These distributions are suited to modeling quantiles, thereby expanding the toolbox of distributions for doubly-bounded random variables. The densities at the boundaries are determined by dispersion and skew parameters, and a third parameter exclusively influences location. The paper also discusses practical issues of parameter estimation and assesses estimation bias, Type I error-rate accuracy, and parameter-estimate collinearity. It also provides three examples of applications to real data using new packages implementing the distributions in R and Stata. [ABSTRACT FROM AUTHOR]

The Hosmer-Lemeshow (HL) test is a commonly used global goodness-of-fit (GOF) test that assesses the quality of the overall fit of a logistic regression model. In this paper, we give results from simulations showing that the type I error rate (and hence power) of the HL test decreases as model complexity grows, provided that the sample size remains fixed and binary replicates (multiple Bernoulli trials) are present in the data. We demonstrate that a generalized version of the HL test (GHL) presented in previous work can offer some protection against this power loss. These results are also supported by application of both the HL and GHL test to a real-life data set. We conclude with a brief discussion explaining the behavior of the HL test, along with some guidance on how to choose between the two tests. In particular, we suggest the GHL test to be used when there are binary replicates or clusters in the covariate space, provided that the sample size is sufficiently large. [ABSTRACT FROM AUTHOR]

The penalized variable selection methods are often used to select the relevant covariates and estimate the unknown regression coefficients simultaneously, but these existing methods may fail to be consistent for the setting with highly correlated covariates. In this paper, the semi-standard partial covariance (SPAC) method with Lasso penalty is proposed to study the generalized linear model with highly correlated covariates, and the consistencies of the estimation and variable selection are shown in high-dimensional settings under some regularity conditions. Some simulation studies and an analysis of colon tumor dataset are carried out to show that the proposed method performs better in addressing highly correlated problem than the traditional penalized variable selection methods. [ABSTRACT FROM AUTHOR]

Generalized linear models (GLMs) are very widely used, but formal goodness-of-fit (GOF) tests for the overall fit of the model seem to be in wide use only for certain classes of GLMs. We develop and apply a new goodness-of-fit test, similar to the well-known and commonly used Hosmer–Lemeshow (HL) test, that can be used with a wide variety of GLMs. The test statistic is a variant of the HL statistic, but we rigorously derive an asymptotically correct sampling distribution using methods of Stute and Zhu (Scand J Stat 29(3):535–545, 2002) and demonstrate its consistency. We compare the performance of our new test with other GOF tests for GLMs, including a naive direct application of the HL test to the Poisson problem. Our test provides competitive or comparable power in various simulation settings and we identify a situation where a naive version of the test fails to hold its size. Our generalized HL test is straightforward to implement and interpret and an R package is publicly available. [ABSTRACT FROM AUTHOR]

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Today, housing prices are constantly rising due to numerous factors. Rising prices have a negative impact on housing sales. If the real value of houses can be determined, price bubbles can be prevented. It is possible to create a realistic price mechanism by examining the factors affecting housing prices. This study focuses on the micro factors affecting house prices. The city of Samsun constitutes the main population of the study. The sample consisted of 5,987 houses for sale (1+1, 2+1, 3+1, and 4+1). The data were obtained from a national website that advertises houses for sale and rent. A model was developed to assess 22 variables believed to affect house prices. Generalized Linear Model was conducted using the hedonic price model. The results showed that square meter size, number of floors, number of bathrooms, district, number of rooms, amount of dues, age of the building, floor, type of heating, being in a complex, title deed status, by whom it is sold, proximity to transportation, view, and neighborhood positively affected house prices. The variables of whether there is a balcony or not, occupancy status, suitability for credit, facade, neighborhood, and house type were found to be insignificant. [ABSTRACT FROM AUTHOR]

To simultaneously improve parameter estimation and variable selection for a target model by the auxiliary information from source models, a weighted likelihood transfer learning (WL-TL), together with a $ l_1 $ l1-penalty, is proposed for high-dimensional generalized linear models. To implement the transfer learning, the relevant techniques, including iterative algorithm and the choice of weight, are suggested. The methodology is computational simple, without need for the bias-correction used in the existing literature of parameter-transfer learning. The theoretical properties such as the quadratic error bound of the parameter estimator and the estimation consistency are established. A specific weight selection method based on the Bayesian decision theory has been proposed and studied. Comprehensive simulation experiments and real data analyzes are conducted to further illustrate the performance of the new method. [ABSTRACT FROM AUTHOR]

It has been suggested that the American black bear (Ursus americanus) may be responsible for a significant number of purported sightings of an alleged unknown species of hominid in North America. Previous analyses have identified a correlation between 'sasquatch' or 'bigfoot' sightings and black bear populations in the Pacific Northwest using ecological niche models and simple models of expected animal sightings. The present study expands the analysis to the entire US and Canada by modelling sasquatch sightings and bear populations in each state/province while adjusting for human population and forest area in a generalized linear model. Sasquatch sightings were statistically significantly associated with bear populations such that, on the average, every 1000 bear increase in the bear population is associated with a 4% (95% CI: 1–7%) increase in sasquatch sightings. Thus, as black bear populations increase, sasquatch sightings are expected to increase. On average, across all states and provinces in 2006, after controlling for human population and forest area, there were approximately 5000 bears per sasquatch sighting. Based on statistical considerations, it is likely that many supposed sasquatch are really misidentified known forms. If bigfoot is there, it could be a bear. [ABSTRACT FROM AUTHOR]

Purpose: When designing an input-output system susceptible to noise, engineers assume a functional relation between the input and the output. The Taguchi method, which uses a dynamic, robust parameter design (RPD) to evaluate the robustness of the input-output relation against noise, is employed. This study aims to address extending the scope of use of a dynamic RPD. Methodology/Approach: A target system in a typical dynamic RPD can be interpreted as one in which the relation between the input and the output is a linear model, and the output error follows a normal distribution. However, an actual system often does not conform to this premise. Therefore, we propose a new analysis approach that can realize a more flexible system design by applying a response surface methodology (RSM) based on a generalized linear model (GLM) to dynamic RPD. Findings: The results demonstrate that 1) a robust solution can be obtained using the proposed method even for a typical dynamic RPD system or an actual system, and 2) the target function can be evaluated using an adjustment parameter. Research Limitation/implication: Further analysis is required to determine which factor(s) in the estimated process model largely contribute(s) to changes in the adjustment parameter. Originality/Value of paper: The applicability of typical dynamic RPD is limited. Hence, this study's analytical process provides engineers with greater design flexibility and deeper insights into dynamic systems across various contexts. [ABSTRACT FROM AUTHOR]

Recently, deep learning has become a pervasive tool in prediction problems for structured and/or unstructured big data in various areas including science and engineering. In particular, deep neural network models (i.e. a basic core model of deep learning) can be viewed as an extension of statistical models by going through the incorporation of hidden layers. In this paper, we study the relationship between both models in terms of model structures and model learning. For this purpose, we also compare the predictive performances of both models, with two practical examples. [ABSTRACT FROM AUTHOR]

Under the framework of generalized linear models (GLM), the generalized estimating equation (GEE) method is typically applied for longitudinal data analysis. However, there are a series of problems due to the misspecification of the within-subject correlation structure, especially in Bayesian estimation. To handle these difficulties, in this paper, we construct a class of generalized estimating equations for longitudinal data with hyperspherical coordinates (HPC) and propose a Bayesian approach established through empirical likelihood (EL). Additionally, an efficient Markov chain Monte Carlo (MCMC) procedure is developed for the required computation of the posterior distribution. As proved by the simulation studies and an application to a real longitudinal data set, our method not only performs better than traditional empirical likelihood estimation and Bayesian estimation with partial autocorrelations (PAC) but also is suitable for non-Gaussian data. [ABSTRACT FROM AUTHOR]

Striped marlin (Tetrapturus audax) is an oceanic pelagic migratory fish. The stock status of striped marlin in the Indian Ocean is now considered to be overfished and subject to overfishing. Quantifying the level of at‐haulback mortality caused by longline fisheries for tuna and tuna‐like species is critical to reducing fishing pressure and protecting the fate of billfish stocks.This study was based on data from 2482 longline fishing operations recorded by Chinese observers in the western Indian Ocean from 2012 to 2019. The dataset includes information on the survival status of 774 striped marlin and their corresponding details. We used a generalized linear model (GLM) to analyse the level of at‐haulback mortality and its potential influencing factors. The results indicate that the distribution of 774 striped marlin had a lower jaw‐fork length (LJFL) range from 130 to 220 cm, and 51.5% of the specimens died at the time of haul‐back. The GLM model revealed that quarter, sea‐surface temperature (SST), hook type, LJFL, chlorophyll (CHL) and longitude had significant effects on at‐haulback condition when the fish were retrieved on board, with the quarter and sea surface temperature having the most significant effects. The interaction term between hook type and LJFL also had a significant effect on at‐haulback mortality, with the model predictions showing that mortality increased with LJFL when using circle hooks but decreased when using Japanese tuna hooks.There has been limited observational analysis of hooking mortality rates for striped marlin, and the present study may provide an important reference for the conservation and management of striped marlin stocks in the Indian Ocean. [ABSTRACT FROM AUTHOR]

Soil, as a valuable natural resource, provides a large number of services and plays an important role in the environment and world economy. Soil degradation and erosion reduce the quality and quantity of the soil and are important natural and anthropogenic processes that affect many countries. Water erosion is the most common type of soil degradation in the world, and Asia has about 50% of the total water erosion area of the world. Gullies are a typical erosion type, and gully formation is an important process of soil erosion and degradation in semi-arid and arid areas, especially areas impacted by human activities and land uses. Because of arid and semi-arid climate, piping and gully erosion is an active phenomenon in the agricultural lands, bare land, and rangeland areas of the Shazand watershed, Markazi Province, central Iran. The goal of this research was to identify the priority conditioning factors of gully erosion, map the susceptibility of the Shazand watershed to gully erosion, and compare some of the applied machine learning techniques based on their accuracy. Prioritization of conditioning factors using a random forest (RF) algorithm demonstrated that distance from the roads, altitude, and rainfall has the greatest impact on gully occurrence in the Shazand watershed. The RF, boosted regression tree (BRT), functional discriminant analysis (FDA), generalized linear model (GLM), and mixture discriminant analysis (MDA) algorithms were applied to create gully erosion susceptibility maps in the study area. The receiver operating characteristic curve (ROC) and area under the curve (AUC) performance metrics were used to validate susceptibility maps. The AUC values of 0.850, 0.831, 0.760, 0.751, and 0.758 were achieved for the RF, BRT, FDA, GLM, and MDA algorithms, respectively. Due to the negative and destructive effects of gully erosion, its management and control is a critical component in the management of natural resources and land uses. Determining the importance of factors affecting erosion is very important to manage and reduce the erosion in the study area. The susceptibility maps of gully erosion prepared in this study are a substantial information resource for decision makers, planners, and engineers concerned with human impacts on natural resources and land uses. About 40% of the study area has high to very high susceptibility to the gully erosion, so control and management of this phenomenon is very important in Shazand watershed. The areas identified with high and very high erosion susceptibility in the Shazand watershed need more care to mitigate the consequences of gully erosion and soil degradation. Also, prioritizing factors will increase the focus on more important factors, and management activities will be more successful. [ABSTRACT FROM AUTHOR]

This research paper investigated multivariate risk assessment in insurance, focusing on four risks of a singular person and their interdependence. This research examined various risk indicators in non-life insurance which was under-writing for organizations with clients that purchase several non-life insurance policies. The risk indicators are probabilities of frequency claims and correlations of two risk lines. The closed forms of probability mass functions evaluated the probabilities of frequency claims. Three generalized linear models of four-variate Sarmanov distributions were proposed for marginals, incorporating various characteristics of policyholders using explanatory variables. All three models were discrete models that were a combination of Poisson and Gamma distributions. Some properties of four-variate Sarmanov distributions were explicitly shown in closed forms. The dataset spanned a decade and included the exposure of each individual to risk over an extended period. The correlations between the two risk types were evaluated in several statistical ways. The parameters of the three Sarmanov model distributions were estimated using the maximum likelihood method, while the results of the three models were compared with a simpler fourvariate negative binomial generalized linear model. The research findings showed that Model 3 was the most accurate of all three models since the AIC and BIC were the lowest. In terms of the correlation, it was found that the risk of claiming auto insurances was related to claiming home insurances. Model 1 could be used for the risk assessment of an insurance company that had customers who held multiple types of insurances in order to predict the risks that may occur in the future. When the insurance company can forecast the risks that may occur in the future, the company will be able to calculate appropriate insurance premiums. [ABSTRACT FROM AUTHOR]

Abstract The impact of heatwaves (HWs) on human health is a topic of growing interest due to the global magnification of these phenomena and their substantial socio‐economic impacts. As for other countries of Southern Europe, Spain is a region highly affected by heat and its increase under climate change. This is observed in the mean values and the increasing incidence of extreme weather events and associated mortality. Despite the vast knowledge on this topic, it remains unclear whether specific types and characteristics of HW are particularly harmful to the population and whether this shows a regional interdependency. The present study provides a comprehensive analysis of the relationship between HW characteristics and mortality in 12 Spanish cities. We used separated time series analysis in each city applying a quasi‐Poisson regression model and distributed lag linear and non‐linear models. Results show an increase in the mortality risk under HW conditions in the cities with a lower HW frequency. However, this increase exhibits remarkable differences across the cities under study not showing any general pattern in the HW characteristics‐mortality association. This relationship is shown to be complex and strongly dependent on the local properties of each city pointing out the crucial need to examine and understand on a local scale the HW characteristics and the HW‐mortality relationship for an efficient design and implementation of prevention measures.

Emerging economies and ecosystems are critically dependent on fossil fuels, and a country’s energy dependence is a significant measure of its reliance on foreign suppliers. This study evaluates the impact of energy reliance on energy intensity, CO2 emission intensity, and the utilization of renewable resources in 35 developing and 20 developed nations, as well as the connection between renewable energy (REN), GDP growth, and CO2 emissions. This study employs the generalized linear model (GLM) and the robust least squares (RLS) method to assess the inverse association between renewable energy and developed and developing economy policymakers, utilizing unique linear panel estimate approaches (1970–2022). The impact of renewable energy as a response variable on economic growth, energy consumption, and CO2 emissions across four continents is investigated in this study. The findings indicate that developing countries experience a rise in per capita CO2 emissions if their renewable energy use exceeds their capacity. This finding remains significant even when other proxies for renewable energy use are introduced using modified approaches. Furthermore, it is particularly relevant to industrialized nations that possess more developed institutions. Even more surprisingly, in terms of the energy and emission intensity required for growth, energy dependence has accelerated all components. The regional analysis revealed a spillover impact in most areas, suggesting that the consequences of energy dependence are essentially the same in neighboring countries. The growth of the renewable energy sector and the decrease in greenhouse gas emissions depend critically on the ability of regional energy exchange unions to mitigate the negative environmental and economic impacts of energy dependency. These underdeveloped countries need to spend more on research and development to catch up technologically.

Background: There is mixed evidence on the extent of association between the allocation of public revenue for healthcare and its indicators of need. Objective: In this study, we examined the relationship between allocations through state health financing (SHF) and the Central Government with infant mortality. Materials and Methods: District-wise infant mortality rate (IMR) was computed using National Family Health Survey-4 data. State-wise data for health budgets through SHF and National Health Mission (NHM, a Centrally Sponsored Scheme), were obtained for the year 2015-16. We used a multivariable analysis through generalized linear model method using identity-link function. Results: We found per capita SHF (₹3169) to be more than 12 times that of public health spending per capita through NHM (₹261). IMR was lower in districts with higher SHF allocation, although statistically insignificant. The allocation through NHM was higher in districts with higher IMR, which is statistically significant. Every unit percentage increase in per capita net state domestic product and female literacy led to 0.31% and 0.54% decline, while a 1% increase in under-five diarrhoea prevalence led to 0.17% increase in IMR. Conclusion: The NHM has contributed to enhancing vertical equity in health-care financing. The States' need to be more responsive to the differences in districts while allocating health-care resources. There needs to be a focus on spending on social determinants, which should be the cornerstone for any universal health coverage strategy.

The estimation of heterogeneous treatment effects has attracted considerable interest in many disciplines, most prominently in medicine and economics. Contemporary research has so far primarily focused on continuous and binary responses where heterogeneous treatment effects are traditionally estimated by a linear model, which allows the estimation of constant or heterogeneous effects even under certain model misspecifications. More complex models for survival, count, or ordinal outcomes require stricter assumptions to reliably estimate the treatment effect. Most importantly, the noncollapsibility issue necessitates the joint estimation of treatment and prognostic effects. Model-based forests allow simultaneous estimation of covariate-dependent treatment and prognostic effects, but only for randomized trials. In this paper, we propose modifications to model-based forests to address the confounding issue in observational data. In particular, we evaluate an orthogonalization strategy originally proposed by Robinson (1988, Econometrica) in the context of model-based forests targeting heterogeneous treatment effect estimation in generalized linear models and transformation models. We found that this strategy reduces confounding effects in a simulated study with various outcome distributions. We demonstrate the practical aspects of heterogeneous treatment effect estimation for survival and ordinal outcomes by an assessment of the potentially heterogeneous effect of Riluzole on the progress of Amyotrophic Lateral Sclerosis. [ABSTRACT FROM AUTHOR]

This paper introduces an alternative linear regression model for over-dispersed count responses with appropriate covariates. It is an extended work of univariate Poisson-Modification of the Quasi Lindley (PMQL) distribution via the generalized linear model approach. A re-parametrized PMQL distribution is considered to demonstrate the flexible properties of the distribution on its regression model. Further, the performance of its maximum likelihood estimation method is examined by a simulation study based on the asymptotic theory. The maximum likelihood estimator is used to estimate the parameters of the regression model. Finally, three simulated data sets and a real-world data set are taken to show the applicability of the PMQL regression model against the Poisson, Negative binomial (NB), Poisson-Quasi Lindley (PQL), and Generalized Poisson-Lindley (GPL) regression models. The results of applications show that the newly introduced model provides a better fit for over-dispersed count responses with covariates than the Poisson, NB, PQL, GPL regression models. [ABSTRACT FROM AUTHOR]

The parameter κ is a general agreement structure used across many fields, such as medicine, machine learning and the pharmaceutical industry. A popular estimator for κ is Cohen's κ ; however, this estimator does not account for multiple influential factors. The primary goal of this paper is to propose an estimator of agreement for a binary response using a logistic regression framework. We use logistic regression to estimate the probability of a positive evaluation while adjusting for factors. These predicted probabilities are then used to calculate expected agreement. It is shown that ignoring needed adjustment measures, as in Cohen's κ , leads to an inflated estimate of κ and a situation similar to Simpson's paradox. Simulation studies verified mathematical relationships and confirmed estimates are inflated when necessary covariates are left unadjusted. Our method was applied to an Alzheimer's disease neuroimaging study. The proposed approach allows for inclusion of both categorical and continuous covariates, includes Cohen's κ as a special case, offers an alternative interpretation, and is easily implemented in standard statistical software. [ABSTRACT FROM AUTHOR]

The earthquake in Bener Meriah Regency on July 2, 2013 with 6.1 Mw tectonic caused physical damage and construction losses to 1,778 typical confined masonry residential buildings. One of the assessments of damage caused by the earthquake was the development of a fragility curve that can be formed by empirical methods based on the Generalized Linear Model (GLM) procedure with log it, pro bit and complementary log-log link functions. This study used data from 9 sub-regencies to identify light, medium and heavy damage levels. Data processing uses ArcGIS and MATLAB software based on data obtained from secondary data survey results and USGS Earthquakes. One of the resulting seismic fragility curves, the pro bit link model, gives a high probability at low and medium PGA intensities for light, medium and heavy damage. The logit link model provides a high probability of high PGA intensities for light, medium, and heavy damage. On the basis of the goodness of fit measurement results, the link pro bit model has the smallest standardized residual error value, which shows the best model of the method because probit has values of 20.16%, 21.12%, and 21.83% in DS1, DS2, and DS3 compared to the link logit and complementary clog-log models. [ABSTRACT FROM AUTHOR]

Glycyrrhiza uralensis is an important endangered medicinal plant species and requires special conservation in China. Its relationships with environmental gradients are significant for its conservation. The responses of dominant species to environmental gradients in G. uralensis communities were simulated by using the general linear model (GLM). The data were collected from 100 quadrats of 5 m x 5 m in five sites from east to west in northern China. The results showed that GLM was very useful in modelling species responses to environmental gradients. Precipitation and soil nutrients (soil N, P, K, organic matter and pH) were the variables that correlated most to G. uralensis populations, dominant species and communities. G. uralensis showed the greatest responses to all comprehensive and single gradients, and was absolutely dominant in communities. Its response pattern was unimodal, i.e. it increased and then decreased after reaching the highest value, to almost all environmental gradients. Other four dominant species showed different response patterns with slight variation. To protect G. uralensis populations and communities, the measures of soil and water conservation, improving soil nutrients, utilization management etc., should be considered. [ABSTRACT FROM AUTHOR]