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We give two prediction intervals for Generalized Linear Models that take model selection uncertainty into account. The first is a straightforward extension of asymptotic normality results and the second includes an extra optimization that improves nominal coverage for small-to-moderate samples. Both PI's are wider than would be obtained without incorporating model selection uncertainty. We compare these two PI's with three other PI's. Two are based on bootstrapping procedures and the third is based on a PI from Bayes model averaging. We argue that for general usage the optimized asymptotic normality PI's work best unless sample sizes are large in which case the PI's based only on asymptotic arguments that include model selection will be easier and equivalent. In an Appendix we extend our results to Generalized Linear Mixed Models. [ABSTRACT FROM AUTHOR]

Objective: At unsignalized T-intersections, right turning drivers from major or minor roads have to accept or reject the available gap and cross the intersection. Poor judgment may arise risk of collision with the major road conflicting vehicles. Subject drivers intending to cross the intersection encounter wide range of gaps. Drivers accept large gaps and reject small gaps. However, drivers experience dilemma over a wide range of gaps. This study aims to examine dilemma experienced by the right turning drivers by modeling gap acceptance and rejection decisions for estimating dilemma zone boundaries on major road. Methods: Videographic method was considered for collecting traffic movement data at unsignalized T-intersections. Traffic video data was collected at three specific unsignalized T-intersections with varying degree of channelization. After this, accepted and rejected gaps with different traffic characteristics of offending vehicles (subject drivers) and conflicting vehicles such as speed, distance, vehicle type, etc. for right turning movements were extracted from the video data to analyze gap acceptance and rejection decisions of the drivers and estimate dilemma zone boundaries. Results: Gap decisions (acceptance or rejection) were modeled using Generalized Linear Model (GLM) as a function of conflicting vehicle type, speed, and distance from the intersection along with degree of channelization for major and minor roads. The results showed that gap rejection probability increased with increment in conflicting vehicle's speed and decrement in conflicting vehicle's distance from the intersection. Dilemma zone boundaries were obtained using developed GLM models by estimating the conflicting vehicle distance from the intersection for 90% and 10% gap rejection probabilities. Dilemma zone boundaries were observed to shift farther from the intersection with increase in vehicle speed and vehicle size. The analysis revealed that subject drivers experienced more dilemma while accepting a gap from major road as compared to minor road. Conclusions: This study showed that channelization plays a major role in mitigating dilemma of the subject drivers. Overall, the study identified dilemma zone boundaries for unsignalized T-intersections which may assist the right turning subject drivers to cross the intersection safely. [ABSTRACT FROM AUTHOR]

Background: The fixed effects model is a useful alternative to the mixed effects model for analyzing stepped-wedge cluster randomized trials (SW-CRTs). It controls for all time-invariant cluster-level confounders and has proper control of type I error when the number of clusters is small. While all clusters in a SW-CRT are typically designed to crossover from the control to receive the intervention, some trials can end with unexposed clusters (clusters that never receive the intervention), such as when a trial is terminated early due to safety concerns. It was previously unclear whether unexposed clusters would contribute to the estimation of the intervention effect in a fixed effects analysis. However, recent work has demonstrated that including an unexposed cluster can improve the precision of the intervention effect estimator in a fixed effects analysis of SW-CRTs with continuous outcomes. Still, SW-CRTs are commonly designed with binary outcomes and it is unknown if those previous results extend to SW-CRTs with non-continuous outcomes. Methods: In this article, we mathematically prove that the inclusion of unexposed clusters improves the precision of the fixed effects intervention effect estimator for SW-CRTs with binary and count outcomes. We then explore the benefits of including an unexposed cluster in simulated datasets with binary or count outcomes and a real palliative care data example with binary outcomes. Results: The simulations show that including unexposed clusters leads to tangible improvements in the precision, power, and root mean square error of the intervention effect estimator. The inclusion of the unexposed cluster in the SW-CRT of a novel palliative care intervention with binary outcomes yielded smaller standard errors and narrower 95% Wald Confidence Intervals. Conclusions: In this article, we demonstrate that the inclusion of unexposed clusters in the fixed effects analysis can lead to the improvements in precision, power, and RMSE of the fixed effects intervention effect estimator for SW-CRTs with binary or count outcomes. [ABSTRACT FROM AUTHOR]

Background: The prognosis of non-small cell lung cancer (NSCLC) is substantially affected by lymph node metastasis (LNM), but there are no noninvasive, inexpensive methods of relatively high accuracy available to predict LNM in NSCLC patients. Methods: Clinical data on NSCLC patients were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Risk factors for LNM were recognized LASSO and multivariate logistic regression. Six predictive models were constructed with machine learning based on risk factors. The area under the receiver operating characteristic curve (AUC) was used to assess the performance of the model. Subgroup analysis with different T-stages was performed on an optimal model. A webpage LNM risk calculator for optimal model was built using the Shinyapps.io platform. Results: We enrolled 64,012 NSCLC patients, of whom 26,611 (41.57%) had LNM. Using multivariate logistic regression, we finally identified 10 independent risk factors for LNM: age, sex, race, histology, primary site, grade, T stage, M stage, tumor size, and bone metastases. GLM is the optimal model among all six machine learning models in both the training and validation cohorts. Subgroup analyses revealed that GLM has good predictability for populations with different T staging. A webpage LNM risk calculator based on GLM was posted on the shinyapps.io platform (https://wubopredict.shinyapps.io/dynnomapp/). Conclusion: The predictive model based on GLM can be used to precisely predict the probability of LNM in NSCLC patients, which was proven effective in all subgroup analyses according to T staging. [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]

AbstractSmoothing splines play a crucial role as a prevalent tool for estimating unknown functions in non parametric regression. In fitting functions with varying roughness, adaptive smoothing splines provide the flexibility to adjust the smoothing parameter adaptively with the changes over the predictor variable within specific intervals, facilitating accommodation for changing roughness. Despite its intuitive appeal and high flexibility, the implementation of this estimator poses challenges, especially in the absence of specified knot numbers, knot locations, and certain simplifying assumptions on the smoothing parameter. D-optimal designs are considered to specify the knot locations in this models. The prior information from data as the prior curvature knowledge can be useful for this optimization problem. For this purpose, the smoothing parameter in this optimization problem can be considered as a function based on the prior curvature. We apply this method to responses from the exponential family of distributions. The mathematical expression of the estimator of the curve in a generalized linear mixed model enables us to derive an information matrix. The derived information matrix and hence the optimum design will depend on the values of the model’s unknown parameters. In such situations, numerical techniques should be considered for evaluating and optimizing this criterion. [ABSTRACT FROM AUTHOR]

Health disparities are ongoing challenges in the United States, with one of the underlying factors being the variation in health risk behaviors across different socioeconomic and demographic communities. In this cross-sectional ecological study, utilizing data from 26,781 US zip codes provided by the Centers for Disease Control and Prevention (CDC), we conducted generalized linear model regressions to explore the associations between race, poverty, education, and urban/rural status in areas. Our analysis indicated that areas with a higher prevalence of Black population and higher poverty levels are associated with a more significant number of unique health risk behaviors. Conversely, other racial groups were associated with fewer unique health risk behaviors, although they may still engage in higher levels of specific behaviors. These insights underscore the need for tailored public health strategies to address the disparities in health risk behaviors across different sub-populations. Additionally, we employed Finite Mixture Modeling (FMM) to identify distinct sub-populations in the United States based on health risk behaviors, specifically binge drinking, smoking, sleep deprivation, and physical inactivity, which are essential risk factors for chronic diseases such as cancer, diabetes, and cardiovascular diseases. We identified four clusters and conducted an in-depth analysis of the demographics and socioeconomic characteristics of the identified clusters. Our findings reveal that one cluster, characterized by the worst health risk behaviors, exhibited the highest poverty rate, the least favorable health insurance profile, and the lowest frequencies of post-secondary education attainment compared to the other three clusters. This cluster, therefore, warrants targeted public health interventions. [ABSTRACT FROM AUTHOR]

The restricted mean survival time (RMST) is often of direct interest in clinical studies involving censored survival outcomes. It describes the area under the survival curve from time zero to a specified time point. When data are subject to length-biased sampling, as is frequently encountered in observational cohort studies, existing methods cannot estimate the RMST for various restriction times through a single model. In this article, we model the RMST as a continuous function of the restriction time under the setting of length-biased sampling. Two approaches based on estimating equations are proposed to estimate the time-varying effects of covariates. Finally, we establish the asymptotic properties for the proposed estimators. Simulation studies are performed to demonstrate the finite sample performance. Two real-data examples are analyzed by our procedures. [ABSTRACT FROM AUTHOR]

Counts of a specific characteristic were obtained within regions defined on an object that was manufactured in a proprietary setting. The count regions were altered during production and resulted in misaligned or overlapping count data. A closed-formula maximum likelihood estimator (MLE) of the new region means is derived using all of the available count data and an independent Poisson model. The MLE is shown to be preferable to estimators constructed using generalized linear models for the overlapping data setting. This closed-form estimator extends to over-dispersed overlapping count data as the quasi-MLE and also performs well with correlated overlapping count data. Standard errors for the estimator are approximated and are validated with a simulation study. Additionally, the methods are extended to overlapping multinomial data. Illustrative examples of the methods are provided throughout the paper and are reproducible with the supplemental R code. Proofs of the paper's results are also included in the supplemental material. [ABSTRACT FROM AUTHOR]

Banana (Musa sp.) is one of the world's most planted and consumed crops. Analysis of plantations using a geospatial perspective is growing in Costa Rica, and it can be used to optimize environmental analysis. The aim of this study was to propose a methodology to identify areas prone to water accumulation to quantify nitrate concentrations using geospatial modeling techniques in a 40 ha section of a banana plantation located in Siquirres, Limón, Costa Rica. A total of five geomorphometric variables (Slope, Slope Length factor (LS factor), Terrain Ruggedness Index (TRI), Topographic Wetness Index (TWI), and Flow Accumulation) were selected in the geospatial model. A 9 cm resolution digital elevation model (DEM) derived from unmanned aerial vehicles (UAVs) was employed to calculate geomorphometric variables. ArcGIS 10.6 and SAGA GIS 7.8.2 software were used in the data integration and analysis. The results showed that Slope and Topographic Wetness Index (TWI) are the geomorphometric parameters that better explained the areas prone to water accumulation and indicated which drainage channels are proper areas to sample nitrate values. The average nitrate concentration in high-probability areas was 8.73 ± 1.53 mg/L, while in low-probability areas, it was 11.28 ± 2.49 mg/L. Despite these differences, statistical analysis revealed no significant difference in nitrate concentrations between high- and low-probability areas. The method proposed here allows us to obtain reliable results in banana fields worldwide. [ABSTRACT FROM AUTHOR]

Species–environment relationships have been extensively explored through species distribution models (SDM) and species abundance models (SAM), which have become key components to understand the spatial ecology and population dynamics directed at biodiversity conservation. Nonetheless, within the internal structure of species' ranges, habitat suitability and species abundance do not always show similar patterns, and using information derived from either SDM or SAM could be incomplete and mislead conservation efforts. We gauged support for the abundance–suitability relationship and used the combined information to prioritize the conservation of South American dwarf caimans (Paleosuchus palpebrosus and P. trigonatus). We used 7 environmental predictor sets (surface water, human impact, topography, precipitation, temperature, dynamic habitat indices, soil temperature), 2 regressions methods (Generalized Linear Models—GLM, Generalized Additive Models—GAM), and 4 parametric distributions (Binomial, Poisson, Negative binomial, Gamma) to develop distribution and abundance models. We used the best predictive models to define four categories (low, medium, high, very high) to plan species conservation. The best distribution and abundance models for both Paleosuchus species included a combination of all predictor sets, except for the best abundance model for P. trigonatus which incorporated only temperature, precipitation, surface water, human impact, and topography. We found non‐consistent and low explanatory power of environmental suitability to predict abundance which aligns with previous studies relating SDM‐SAM. We extracted the most relevant information from each optimal SDM and SAM and created a consensus model (2,790,583 km2) that we categorized as low (39.6%), medium (42.7%), high (14.9%), and very high (2.8%) conservation priorities. We identified 279,338 km2 where conservation must be critically prioritized and only 29% of these areas are under protection. We concluded that optimal models from correlative methods can be used to provide a systematic prioritization scheme to promote conservation and as surrogates to generate insights for quantifying ecological patterns. [ABSTRACT FROM AUTHOR]

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]

Regularized regression methods have attracted much attention in the literature, mainly due to its application in high-dimensional variable selection problems. Most existing regularization methods assume that the predictors are directly observed and precisely measured. It is well known that in a low-dimensional regression model if some covariates are measured with error, then the naive estimators that ignore the measurement error are biased and inconsistent. However, the impact of measurement error in regularized estimation procedures is not clear. For example, it is known that the ordinary least squares estimate of the regression coefficient in a linear model is attenuated towards zero and, on the other hand, the variance of the observed surrogate predictor is inflated. Therefore, it is unclear how the interaction of these two factors affects the selection outcome. To correct for the measurement error effects, some researchers assume that the measurement error covariance matrix is known or can be estimated using external data. In this paper, we propose the regularized instrumental variable method for generalized linear measurement error models. We show that the proposed approach yields a consistent variable selection procedure and root-n consistent parameter estimators. Extensive finite sample simulation studies show that the proposed method performs satisfactorily in both linear and generalized linear models. A real data example is provided to further demonstrate the usage of the method. [ABSTRACT FROM AUTHOR]

Abstract Background The prognosis of non-small cell lung cancer (NSCLC) is substantially affected by lymph node metastasis (LNM), but there are no noninvasive, inexpensive methods of relatively high accuracy available to predict LNM in NSCLC patients. Methods Clinical data on NSCLC patients were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Risk factors for LNM were recognized LASSO and multivariate logistic regression. Six predictive models were constructed with machine learning based on risk factors. The area under the receiver operating characteristic curve (AUC) was used to assess the performance of the model. Subgroup analysis with different T-stages was performed on an optimal model. A webpage LNM risk calculator for optimal model was built using the Shinyapps.io platform. Results We enrolled 64,012 NSCLC patients, of whom 26,611 (41.57%) had LNM. Using multivariate logistic regression, we finally identified 10 independent risk factors for LNM: age, sex, race, histology, primary site, grade, T stage, M stage, tumor size, and bone metastases. GLM is the optimal model among all six machine learning models in both the training and validation cohorts. Subgroup analyses revealed that GLM has good predictability for populations with different T staging. A webpage LNM risk calculator based on GLM was posted on the shinyapps.io platform ( https://wubopredict.shinyapps.io/dynnomapp/ ). Conclusion The predictive model based on GLM can be used to precisely predict the probability of LNM in NSCLC patients, which was proven effective in all subgroup analyses according to T staging.

Extended Abstract Background: Oriental beech (Fagus orientalis Lipsky) is one of the well-known industrial species of Hyrcanian forests that spreads from the west to east of these forests. Human activities have caused significant damage to Hyrcanian forests, and this issue highlights the importance of identifying factors affecting the structural characteristics of trees, especially beech, for the optimal management of these forests and the restoration of degraded areas. Considering the effect of environmental factors on the structural characteristics (the basal area and number per hectare) and the distribution of trees, the knowledge of their ecological requirements can be used to prepare programs for the protection and development of forests. Previous research about factors affecting the distribution of this species focused on topographical variables and the effects of climate change. Thus, No research has so far investigated the effect of the frequency of fog occurrence on the structural characteristics of this species. Therefore, the effect of topographical variables (height above sea level, slope, and direction), temperature, relative humidity, and fog on the number per hectare, basal area, and the presence of beech species was investigated in the present study using a generalized linear model (GLM). The results of this research and other studies on the future data from the forests of northern Iran can help predict possible changes in the distribution of species, especially beech species, under the effect of global warming. Methods: To carry out this research, beech species data were obtained from the forest inventory data bank in the north of Iran. Because no meteorological station was available for the whole region of Hyrcanian forests, meteorological data were obtained from the POWER project of the National Aeronautics and Space Administration of the USA. Since fog is defined as water droplets suspended near the earth's surface, which reduces horizontal visibility to less than one kilometer, the occurrence of fog was determined using the horizontal visibility index. After preparing the data, first the number of trees per hectare and then the basal area of trees were calculated in each sample plot. The collected data were separated based on the presence or absence of beech. Data were analyzed using GLM and cross-validation evaluation in R software and the caret package. Results: The results of the correlation between the independent variables showed that the amount of precipitation had a significant and high correlation with relative humidity, hence the precipitation variable was removed from the modeling. The GLM showed acceptable accuracy for predicting the number of trees per hectare (R2 = 0.16), basal area (R2 = 0.16), and the presence of beech species (AUC = 0.75). The fog, height above sea level, and relative air humidity variables had a positive relationship with all three traits (number per hectare, basal area, and the presence of beech species) while temperature was negatively related only to the presence of beech species. The aspect variable had a positive and significant relationship with the number of beech trees per hectare and basal area, respectively, but it was not significantly related to the presence of this species. The height above sea level was the most important variable for predicting the number of trees per hectare, the basal area, and the presence of beech species. Relative humidity was the second most important variable for predicting the number of trees per hectare and basal area while fog was the second most important variable for the presence of beech species. The temperature, slope, and aspect had a significance of less than 20%. Conclusion: The reason for the higher relative importance of the height above sea level can be attributed to low temperature and high rainfall as the optimal conditions for the growth of the beech tree; these conditions are directly related to the height above sea level. Our results also determine the importance of relative humidity and the occurrence of fog in the distribution of beech species. The high importance of the relative humidity variable may result from the fact that beech is generally described as adapting to full-shade to partial-shade conditions. Since the northern aspect (in the Northern Hemisphere) generally receives less direct sunlight and is usually cooler, this species usually establishes in the northern direction in the Hyrcanian forest. On the other hand, since relative humidity increases with decreasing temperature, it can be concluded that beech prefers higher relative humidity. In the forests of mountainous areas and near the coast, the number of fog occurrences is high due to high air humidity and altitude. Finally, this phenomenon leads to rain fog and increases the amount of water reaching the forest floor. Therefore, it can be stated that the occurrence and production of fog play a pivotal role in providing the water needs of beech trees due to the high moisture requirement of this species. The high importance of relative air humidity and the occurrence of fog in the distribution of beech species necessitate additional studies regarding the accurate estimation of the occurrence and amount of fog in Hyrcanian forests. Studies on fog and its effects on vegetation, particularly in temperate regions, can provide valuable insight into the potential benefits of fog for this species. Given the lack of studies, it is suggested to investigate the effects of other environmental factors, including soil properties, on the distribution of beech species.

Banana (Musa sp.) is one of the world’s most planted and consumed crops. Analysis of plantations using a geospatial perspective is growing in Costa Rica, and it can be used to optimize environmental analysis. The aim of this study was to propose a methodology to identify areas prone to water accumulation to quantify nitrate concentrations using geospatial modeling techniques in a 40 ha section of a banana plantation located in Siquirres, Limón, Costa Rica. A total of five geomorphometric variables (Slope, Slope Length factor (LS factor), Terrain Ruggedness Index (TRI), Topographic Wetness Index (TWI), and Flow Accumulation) were selected in the geospatial model. A 9 cm resolution digital elevation model (DEM) derived from unmanned aerial vehicles (UAVs) was employed to calculate geomorphometric variables. ArcGIS 10.6 and SAGA GIS 7.8.2 software were used in the data integration and analysis. The results showed that Slope and Topographic Wetness Index (TWI) are the geomorphometric parameters that better explained the areas prone to water accumulation and indicated which drainage channels are proper areas to sample nitrate values. The average nitrate concentration in high-probability areas was 8.73 ± 1.53 mg/L, while in low-probability areas, it was 11.28 ± 2.49 mg/L. Despite these differences, statistical analysis revealed no significant difference in nitrate concentrations between high- and low-probability areas. The method proposed here allows us to obtain reliable results in banana fields worldwide.

[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.

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]

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 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]

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 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]

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]

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]

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]

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]

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 $ l 1 -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]

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]

This article represents a further step in the continuously developing process of improving the prediction capabilities using databases. Its aim is to compare and evaluate the operation, performance and validity of knowledge extraction techniques related to prediction. The innovative part of this study concerns the selection, enrichment and processing of the database used. In particular, the database contains consumption data for an entire city over the course of a year. These data were then enriched with elements concerning the determination of the time and the environmental conditions, in order to take into consideration the correlation of the data with these parameters. Subsequently, after being converted into an editable format, they were processed using techniques such as normalization and factor analysis, which finally led to the prediction process. At this stage, different methods, such as decision trees, deep learning and generalized linear models, were applied and thoroughly analyzed, and both their operation and their effectiveness were compared and evaluated. The present effort, therefore, intends to provide a useful tool that will contribute to future efforts to improve predictions from existing data. [ABSTRACT FROM AUTHOR]

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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]

Abstract Species–environment relationships have been extensively explored through species distribution models (SDM) and species abundance models (SAM), which have become key components to understand the spatial ecology and population dynamics directed at biodiversity conservation. Nonetheless, within the internal structure of species' ranges, habitat suitability and species abundance do not always show similar patterns, and using information derived from either SDM or SAM could be incomplete and mislead conservation efforts. We gauged support for the abundance–suitability relationship and used the combined information to prioritize the conservation of South American dwarf caimans (Paleosuchus palpebrosus and P. trigonatus). We used 7 environmental predictor sets (surface water, human impact, topography, precipitation, temperature, dynamic habitat indices, soil temperature), 2 regressions methods (Generalized Linear Models—GLM, Generalized Additive Models—GAM), and 4 parametric distributions (Binomial, Poisson, Negative binomial, Gamma) to develop distribution and abundance models. We used the best predictive models to define four categories (low, medium, high, very high) to plan species conservation. The best distribution and abundance models for both Paleosuchus species included a combination of all predictor sets, except for the best abundance model for P. trigonatus which incorporated only temperature, precipitation, surface water, human impact, and topography. We found non‐consistent and low explanatory power of environmental suitability to predict abundance which aligns with previous studies relating SDM‐SAM. We extracted the most relevant information from each optimal SDM and SAM and created a consensus model (2,790,583 km2) that we categorized as low (39.6%), medium (42.7%), high (14.9%), and very high (2.8%) conservation priorities. We identified 279,338 km2 where conservation must be critically prioritized and only 29% of these areas are under protection. We concluded that optimal models from correlative methods can be used to provide a systematic prioritization scheme to promote conservation and as surrogates to generate insights for quantifying ecological patterns.

Diagnosis of most ophthalmic conditions, such as diabetic retinopathy, generally relies on an effective analysis of retinal blood vessels. Techniques that depend solely on the visual observation of clinicians can be tedious and prone to numerous errors. In this article, we propose a semi-supervised automated approach for segmenting blood vessels in retinal color images. Our method effectively combines some classical filters with a Generalized Linear Model (GLM). We first apply the Curvelet Transform along with the Contrast-Limited Histogram Adaptive Equalization (CLAHE) technique to significantly enhance the contrast of vessels in the retinal image during the preprocessing phase. We then use Gabor transform to extract features from the enhanced image. For retinal vasculature identification, we use a GLM learning model with a simple link identity function. Binarization is then performed using an automatic optimal threshold based on the maximum Youden index. A morphological cleaning operation is applied to remove isolated or unwanted segments from the final segmented image. The proposed model is evaluated using statistical parameters on images from three publicly available databases. We achieve average accuracies of 0.9593, 0.9553 and 0.9643, with Receiver Operating Characteristic (ROC) analysis yielding Area Under Curve (AUC) values of 0.9722, 0.9682 and 0.9767 for the CHASE_DB1, STARE and DRIVE databases, respectively. Compared to some of the best results from similar approaches published recently, our results exceed their performance on several datasets.

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.