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AbstractLogistic regression models have a severe problem called separation. The maximum likelihood estimator does not exist in logistic regression models for data structures under separation. Under separation, the forcibly estimated maximum likelihood estimate may have an extremely large value. Separation often occurs when the size of dataset is small. Consequently, goodness-of-fit measures based on the likelihood ratio and those based on covariance functions using the maximum likelihood estimate indicate that the model is excessively good regardless of the cause of the separation. The Firth and exact logistic regression methods are valid estimation methods for separation problems. Therefore, we propose methods to reasonably evaluate the goodness-of-fit measures of statistical models under separation with dataset of a small sample size with the abovementioned methods. The goodness-of-fit measures based on covariance functions which are a generalization of the multiple correlation coefficient, referred to as the regression correlation coefficient and the entropy coefficient of determination are then used combined with the abovementioned methods for the separation data. In addition, we conducted a data analysis using the definition of the non separation ratio based on the regression depth. [ABSTRACT FROM AUTHOR]

This study examines the relationship between residential exposure to blue spaces (e.g., rivers, lakes, and seas) and cognitive function in middle-aged and older adults in the United Kingdom, with a specific focus on the mediating effects of air pollution, particularly nitrogen dioxide (NO₂) and fine particulate matter (PM 2.5). Cognitive function was assessed using touch screens at UK Biobank Assessment Centre, while residential blue space exposure within a 300-meter radius was estimated from land-use data. Annual average levels of air pollution, specifically NO₂ and PM 2.5 , were calculated through a land-use regression model. Logistic regression models analyzed the association between blue space exposure and cognitive function, and restricted cubic splines were employed to assess potential nonlinear relationships. Causal mediation analysis quantified the indirect effects of air pollution on this relationship. The study included 35,669 participants, revealing that high blue space exposure (≥75 %) was associated with a 13.2 % lower risk of cognitive impairment compared to those with low exposure (<25 %). Notably, NO₂ and PM 2.5 significantly mediated this association, with indirect effects estimated at 9.5 % and 15.85 %, respectively. These findings indicate that increased residential exposure to blue spaces is linked to a reduced risk of cognitive impairment, highlighting the protective role of blue environments in the context of air pollution. This research underscores the importance of environmentally sensitive urban planning and policies to promote public health and cognitive well-being among vulnerable populations. [Display omitted] • Elevated residential blue space level is related to higher cognitive function. • Air pollution performs effect modification and mediation role. • The effect of blue space on cognitive function is partially mediated by NO 2 and PM 2.5. • Multiple statistical methods were used to obtain more comprehensive results. [ABSTRACT FROM AUTHOR]

The notion of sentencehood in Mandarin Chinese is much less well-defined than in many other languages, with a block of clauses often joined by commas without conjunctions and with the period often occurring at the end of a block of clauses to indicate meaning completeness rather than the completeness of a sentential structure. The potential factors that may affect native Chinese speakers' judgment of meaning completeness and perception of sentence boundaries have not yet been systematically examined. In light of this research gap, this study investigates the factors that may play a role in native Chinese speakers' sentence boundary perception. To this end, we conducted text re-punctuation experiments in two separate groups, a training group and a testing group, using different stimuli texts. The stimuli texts were annotated with multiple levels of linguistic information to identify potentially relevant variables that could affect the participants' sentence boundary perception. Logistic regression and the Bayesian statistical methods were applied to test the potential effects of multiple variables on the participants' responses. The logistic regression model trained on the data from the training group achieved a high level of accuracy in predicting the responses by the testing group. The model revealed a more important role of semantic information than syntactic information in the participants' sentence boundary perception. The implications of our findings for understanding the perception of Chinese sentence boundaries are discussed. [ABSTRACT FROM AUTHOR]

Simple Summary: Despite the fact that sex estimation methods from crania are very popular in forensic anthropology, few validation studies have verified their accuracy and reliability in different populations. Different from craniometrics, for which validation studies have remarkably increased lately, the methods based on cranial morphology still need to be thoroughly investigated, even if a large consensus exists on the effects of population variability on sexual cranial dimorphism. When dealing with forensic contexts, appropriately-validated methods should be applied for building accurate biological profiles. Since the possible sexual dimorphism variation of cranial morphological traits needs to be evaluated properly in various populations, in this study, we analyzed the accuracy of existing regression models for predicting sex from cranial morphological traits in an Italian contemporary/modern population. In addition, we propose new logistic regression models that are more accurate and specific for our sample. The results also update the reference standards for populations of this geographical area and provide an additional important warning on sexual dimorphism to anthropologists working in forensic contexts. Although not without subjectivity, the cranial trait scoring method is an easy visual method routinely used by forensic anthropologists in sex estimation. The revision presented by Walker in 2008 has introduced predictive models with good accuracies in the original populations. However, such models may lead to unsatisfactory performances when applied to populations that are different from the original. Therefore, this study aimed to test the sex predictive equations reported by Walker on a contemporary Italian population (177 individuals) in order to evaluate the reliability of the method and to identify potential sexual dimorphic differences between American and Italian individuals. In order to provide new reference data to be used by forensic experts dealing with human remains of modern/contemporary individuals from this geographical area, we designed logistic regression models specific to our population, whose accuracy was evaluated on a validation sample from the same population. In particular, we fitted logistic regression models for all possible combinations of the five cranial morphological traits (i.e., nuchal crest, mastoid process, orbital margin, glabella, and mental eminence). This approach provided a comprehensive set of population-specific equations that can be used in forensic contexts where crania might be retrieved with severe taphonomic damages, thus limiting the application of the method only to a few morphological features. The results proved once again that the effects of secular changes and biogeographic ancestry on sexual dimorphism of cranial morphological traits are remarkable, as highlighted by the low accuracy (from 56% to 78%) of the six Walker's equations when applied to our female sample. Among our fitted models, the one including the glabella and mastoid process was the most accurate since these features are more sexually dimorphic in our population. Finally, our models proved to have high predictive performances in both training and validation samples, with accuracy percentages up to 91.7% for Italian females, which represents a significant success in minimizing the potential misclassifications in real forensic scenarios. [ABSTRACT FROM AUTHOR]

Customer attrition is the phenomenon wherein a customer leaves a service provider. With the growing competition in the service sector, preventing customer attrition has become critical for sustainability, as it is well established that retaining existing customers is more profitable than acquiring new customers (Jacob, 1994). This gives customer attrition analytics the challenging task of predicting which customers are likely to leave, and of subsequently designing and implementing retention programmes for these customers. Customer analytics has made many strides in marketing, employer desirability, and branding, but has so far made limited strides in the recruitment industry space. The objective of the study is to identify the factors affecting a candidate's decision to accept a job opportunity in an organisation, using predictors such as the industry verticals, the candidate's skillsets, workplace location, gender, compensation offered, and the notice period of the candidate. The model developed is a logistic regression model, to determine whether a candidate selected will accept a job opportunity in an organisation or not. The analysis was performed based on a sample of 443 candidates who were provided job offers in the period 2013-2015 by a recruitment service provider. [ABSTRACT FROM AUTHOR]

In this paper a family of Wald-type test statistics for linear hypotheses in the logistic regression model with complex sample survey data is introduced and its properties are explored. The family of tests considered is based on the pseudo minimum phi-divergence estimator that contains, as a particular case, the pseudo maximum likelihood estimator. We obtain the asymptotic distribution and through a simulation study it is shown that some Wald-type tests present much more stable levels than the classical one for high and moderate values of the intra-cluster correlation parameter. [ABSTRACT FROM AUTHOR]

Partial discharge (PD) investigations can identify and localise incipient failures in power transformers early, thus avoiding considerable financial losses. The feature extraction of PD signals is a fundamental step for the development of such location techniques since it directly influences the performance of a location method. This study presents a detailed comparative analysis of four traditional approaches for the obtaining of attributes towards a better set of signal features for the location of PDs. The approaches were critically compared regarding their ability to locate experimentally generated discharges between adjacent layers of a prototype winding. In order to perform such analysis, a localisation structure based on logistic regression models was elaborated, capable of determining both layers and sections of the winding affected by PDs and easily applicable in practice. The results show energy features of wavelet coefficients, obtained through the decomposition of high‐frequency current signals acquired at the winding endings, achieve better performance in the PD localisation, accurately indicating discharge occurrence points among layers and sections of the winding. [ABSTRACT FROM AUTHOR]

This study analyses individual-level determinants of internet banking adoption of customers in Turkey. Using a nationally representative household survey data, logistic regression models are estimated for quantification of the factors which influence consumer's decision of internet banking adoption. Empirical results indicate that females are less likely to use internet banking compared to males in Turkey. Education level, being employed, household income level, frequency, variety and skill levels of internet usage have significantly positive associations with likelihood of using internet banking services. Additionally, age demonstrates a non-linear association with the use of internet banking. Middle-aged Turkish citizens are more likely to employ internet banking tools compared to the young and the elderly. Hence, we conclude that the digital divide exists in the Turkish case and elimination of disparities in technology adoption has the potential to bring substantial benefits to the financial system in Turkey. [ABSTRACT FROM AUTHOR]

This paper explores how Latent Class Models (LCM) can be applied in social research, when the basic assumptions of regression models cannot be validated. We examine the usefulness of this method with data collected from a study on the relationship between bridging social capital and the Internet. Social capital is defined here as the resources that are potentially available in one’s social ties. Bridging is a dimension of social capital, usually related to weak ties (acquaintances), and a source of instrumental resources such as information. The study surveyed a stratified random sample of 417 inhabitants of Lisbon, Portugal. We used LCM to create the variable bridging social capital, but also to estimate the relationship between bridging social capital and Internet usage when we encountered convergence problems with the logistic regression analysis. We conclude by showing a positive relationship between bridging and Internet usage, and by discussing the potential of LCM for social science research. [ABSTRACT FROM AUTHOR]

Highlights: [•] The logistic regression prediction model was developed. [•] It was found that the maintenance personnel and workers have the highest risk. [•] The parts of the body with the highest risk were the upper and lower extremities. [•] Model can be used for various components of the accident. [•] The equipment protecting the lower and upper extremities can be effective in decreasing non-fatal work accidents. [Copyright &y& Elsevier]

Mixture experiments are commonly encountered in many fields including chemical, pharmaceutical and consumer product industries. Due to their wide applications, mixture experiments, a special study of response surface methodology, have been given greater attention in both model building and determination of designs compared with other experimental studies. In this paper, some new approaches are suggested on model building and selection for the analysis of the data in mixture experiments by using a special generalized linear models, logistic regression model, proposed by Chen et al. [7]. Generally, the special mixture models, which do not have a constant term, are highly affected by collinearity in modeling the mixture experiments. For this reason, in order to alleviate the undesired effects of collinearity in the analysis of mixture experiments with logistic regression, a new mixture model is defined with an alternative ratio variable. The deviance analysis table is given for standard mixture polynomial models defined by transformations and special mixture models used as linear predictors. The effects of components on the response in the restricted experimental region are given by using an alternative representation of Cox's direction approach. In addition, odds ratio and the confidence intervals of odds ratio are identified according to the chosen reference and control groups. To compare the suggested models, some model selection criteria, graphical odds ratio and the confidence intervals of the odds ratio are used. The advantage of the suggested approaches is illustrated on tumor incidence data set. [ABSTRACT FROM AUTHOR]

Recently, median regression models have received increasing attention. When continuous responses follow a distribution that is quite different from a normal distribution, usual mean regression models may fail to produce efficient estimators whereas median regression models may perform satisfactorily. In this article, we discuss using median regression models to deal with longitudinal data with dropouts. Weighted estimating equations are proposed to estimate the median regression parameters for incomplete longitudinal data, where the weights are determined by modeling the dropout process. Consistency and the asymptotic distribution of the resultant estimators are established. The proposed method is used to analyze a longitudinal data set arising from a controlled trial of HIV disease ( Volberding et al., 1990 , The New England Journal of Medicine 322, 941–949). Simulation studies are conducted to assess the performance of the proposed method under various situations. An extension to estimation of the association parameters is outlined. [ABSTRACT FROM AUTHOR]

Destructive development of suburban areas in some metropolises has exposed suburban soils to high risk of potentially toxic trace elements (PTEs) enrichment, which also threatens human and ecosystem health. This study investigated the pollution status, sources and spatial patterns of four PTEs (Pb, Cd, Cr and As) in 1805 soil samples collected from the suburbs of Shanghai in 2015. Nineteen potential sources, including: 6 soil property factors, 10 proximity factors and 3 topography factors, were selected to help explain the PTEs aggregation using logistic regression models from global and local perspectives. The statistical results of PTEs concentration revealed that Cd showed the highest pollution risk in local soils, which was followed by As. Soil property was the primary factor affecting the PTEs (except Cr) enrichment, both identified by global models and local models. The local model particularly emphasized the significant correlation between soil property and PTEs in most parts of the outer suburbs and southeastern inner suburbs. Some proximity factors such as distance to district center and water were negatively correlated with Cd pollution and some topography factors such as elevation and slope were closely related to As pollution. It is worth noting that in the coastal areas, especially Chongming Island, there were obvious PTEs depositions in the soil near the estuary. This study helps to identify the sources of anthropogenic contamination and geogenic enrichment of the four PTEs and their spatial patterns, playing an essential role in formulating regional environmental policies for coastal cities. Image 1 • Cd showed the highest pollution risk in the suburban soils and followed by As. • Global models revealed that the soil parent materials affected PTEs enrichment most. • Local model highlighted the spatial variability of PTEs sources. • Arsenic (As) tended to accumulate in areas with low elevation and low slope. • The joint effects of these sources on PTEs in suburbs of Shanghai should be stressed. Main findings: Cd showed the highest pollution risk in Shanghai suburb soils and soil property is the primary factor affecting the PTEs (except Cr) enrichment, both identified by global models and local models. [ABSTRACT FROM AUTHOR]

Objective: Substantial differences exist in the relationship of prostate cancer (PCa) detection rate and prostate-specific antigen (PSA) level between Western and Asian populations. Classic Western risk calculators, European Randomized Study for Screening of Prostate Cancer Risk Calculator, and Prostate Cancer Prevention Trial Risk Calculator, were shown to be not applicable in Asian populations. We aimed to develop and validate a risk calculator for predicting the probability of PCa and high-grade PCa (defined as Gleason Score sum 7 or higher) at initial prostate biopsy in Chinese men.Materials and Methods: Urology outpatients who underwent initial prostate biopsy according to the inclusion criteria were included. The multivariate logistic regression-based Chinese Prostate Cancer Consortium Risk Calculator (CPCC-RC) was constructed with cases from 2 hospitals in Shanghai. Discriminative ability, calibration and decision curve analysis were externally validated in 3 CPCC member hospitals.Results: Of the 1,835 patients involved, PCa was identified in 338/924 (36.6%) and 294/911 (32.3%) men in the development and validation cohort, respectively. Multivariate logistic regression analyses showed that 5 predictors (age, logPSA, logPV, free PSA ratio, and digital rectal examination) were associated with PCa (Model 1) or high-grade PCa (Model 2), respectively. The area under the curve of Model 1 and Model 2 was 0.801 (95% CI: 0.771-0.831) and 0.826 (95% CI: 0.796-0.857), respectively. Both models illustrated good calibration and substantial improvement in decision curve analyses than any single predictors at all threshold probabilities. Higher predicting accuracy, better calibration, and greater clinical benefit were achieved by CPCC-RC, compared with European Randomized Study for Screening of Prostate Cancer Risk Calculator and Prostate Cancer Prevention Trial Risk Calculator in predicting PCa.Conclusions: CPCC-RC performed well in discrimination and calibration and decision curve analysis in external validation compared with Western risk calculators. CPCC-RC may aid in decision-making of prostate biopsy in Chinese or in other Asian populations with similar genetic and environmental backgrounds. [ABSTRACT FROM AUTHOR]