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We discuss maximum likelihood estimation of parameters for models governed by a stochastic differential equation driven by a mixed fractional Brownian motion with random effects. [ABSTRACT FROM AUTHOR]

We develop a method of classified mixed model prediction based on generalized linear mixed models that incorporate pseudo‐prior information to improve prediction accuracy. We establish consistency of the proposed method both in terms of prediction of the true mixed effect of interest and in terms of correctly identifying the potential class corresponding to the new observations if such a class matching one of the training data classes exists. Empirical results, including simulation studies and real‐data validation, fully support the theoretical findings. [ABSTRACT FROM AUTHOR]

The smooth running of business operations demands an efficient management of working capital by properly managing the inventory, accounts receivables and accounts payables of the business. This helps companies not only fulfil their short-term financial commitments but also boost their earnings. Therefore, the present study aims at verifying the impact of working capital management on the profitability of the companies under pharmaceutical industry in India. For empirical analysis, the data of 618 pharmaceutical companies is taken over a period of seven years from 2014-2015 to 2020-2021 and multivariate panel data regression technique is applied on data for estimating the results. The findings of the study validate that the profitability of companies is significantly influenced by working capital management. These findings will provide an insight to corporate managers and owners of pharmaceutical companies in deciding appropriate working capital strategy. [ABSTRACT FROM AUTHOR]

Kwadwo Arhin,1 Albert Opoku Frimpong,2 Kwame Acheampong3 1Department of Economics, Ghana Institute of Management and Public Administration, Accra, Ghana; 2Department of Banking and Finance, University of Professional Studies, Accra, Ghana; 3Department of Accounting Studies Education, Akenten Appiah-Menkah University of Skills Training and Entrepreneurial Development, Kumasi, GhanaCorrespondence: Kwadwo Arhin, Department of Economics, Ghana Institute of Management and Public Administration, Accra, Ghana, Tel +233 246767908, Email arhinkwadwo@gmail.comBackground: Investment in primary health care (PHC) to achieve universal health coverage (UHC) and better health outcomes remains a key global health agenda. This study aimed to assess the effects of PHC spending on UHC and health outcomes.Methods: The study used the Grossman Health Production Model and conducted econometric analyses using panel data from 2016 to 2019 covering 34 countries in SSA. Fixed and random effects panel regression models were used for the analyses. All the analyses in this study were carried out using the statistical software package STATA Version 15.Results: We found that PHC expenditure has a positive significant but inelastic effect on UHC and life expectancy at birth and a negative effect on infant mortality. Both the fixed and random effects models provided a robust relationship between PHC expenditure and UHC and health outcomes. Education, access to an improved water source, and the age structure of the population were found to be strongly associated with health outcomes.Conclusion: The inelastic nature of the PHC expenditure means that the UHC goal might only be achieved at high levels of PHC expenditure. This implies that policymakers must make conscious effort to increase PHC expenditure to ensure the attainment of the UHC goal.Keywords: primary health care expenditure, universal health coverage, health outcomes, fixed effects, random effects, sub-Saharan Africa

This study examines the spatio-temporal effects of the COVID-19 pandemic on shared e-scooter usage by leveraging two years (2019 and 2020) of daily shared micromobility data from Austin, Texas. We employed a series of random effects spatial-autoregressive model with a spatially autocorrelated error (SAC) to examine the differences and similarities in determinants of e-scooter usage during regular and pandemic periods and to identify factors contributing to the changes in e-scooter use during the Pandemic. Model results provided strong evidence of spatial autocorrelation in the e-scooter trip data and found a spatial negative spillover effect in the 2020 model. The key findings are: i) while the daily e-scooter trips reduced, the average trip distance and the average trip duration increased during the Pandemic; ii) the central part of Austin city experienced a major decrease in e-scooter usage during the Pandemic compared to other parts of Austin; iii) areas with low median income and higher number of available e-scooter devices experienced a smaller decrease in daily total e-scooter trips, trip distance, and trip duration during the Pandemic while the opposite result was found in areas with higher public transportation services. The results of this study provide policymakers with a timely understanding of the changes in shared e-scooter usage during the Pandemic, which can help redesign and revive the shared micromobility market in the post-pandemic era.

Sorghum is the most popular crop in arid and semi-arid areas, especially in Sub-Saharan African countries. Genotype effects, environmental and the interaction of genotype by environmental factors have an influence on phenotypic traits. The aim of the study is to identify the relationship between grain yield and other yield-related traits and select the genotypes which perform better in grain yield as well as to examine the association between the uncorrelated phenotypic traits and grain yield via mixed model. The data was generated using a lattice square design. Principal component analysis was used to generate uncorrelated variables for the mixed model. The study revealed that there was a difference in grain yield due to the treatment and there was a pairwise relationship among the phenotypic variables. 77.12% of the total variance of the original phenotypic variables was explained by the first three principal components and decided to use PCAs as input variables for the mixed model. All PCs had significant effects on grain yield as well as grain yield variability due to random effects associated with genotypes, genotype interaction by treatment, and replication within the treatment. The variability of grain yield due to genotype effect was explained about 45.73%, the variation of grain yield due to the interaction of genotype by the treatment was also explained about 39.06% and 1.55% of the variation of grain was explained by replication within treatment. The best performer genotypes recommended for mass production were G40 (Genotype 40), G186 (Genotype 186) and G196 (Genotype 196) without any constraint of environment. The genotypes recommended for mass production under irrigation conditions were G40 (Genotype 40), G62 (Genotype 62) and G192 (Genotype 192). G26 (Genotype 26), G55 (Genotype 55) and G49 (Genotype 49) were the genotypes recommended for mass production under stress conditions. Overall, the study recommends using a mixed model to fit the grain yield, and future work will focus on to evaluate the performance of genotypes under different environments and years of production.

We study a joint model where logistic regression is applied to binary longitudinal data with a mismeasured time-varying covariate that is modeled using a mechanistic nonlinear model. Multiple random effects are necessary to characterize the trajectories of the covariate and the response variable, leading to a high dimensional integral in the likelihood. To account for the computational challenge, we propose a stochastic expectation-maximization (StEM) algorithm with a Gibbs sampler coupled with Metropolis–Hastings sampling for the inference. In contrast with previous developments, this algorithm uses single imputation of the missing data during the Monte Carlo procedure, substantially increasing the computing speed. Through simulation, we assess the algorithm's convergence and compare the algorithm with more classical approaches for handling measurement errors. We also conduct a real-world data analysis to gain insights into the association between CD4 count and viral load during HIV treatment. [ABSTRACT FROM AUTHOR]

Meta-analyses by definition are a subtype of systematic review intended to quantitatively assess the strength of evidence present on an intervention or treatment. Such analyses may use individual-level data or aggregate data to produce a point estimate of an effect, also known as the combined effect, and measure precision of the calculated estimate. The current article will review several important considerations during the analytic phase of a meta-analysis, including selection of effect estimators, heterogeneity and various sub-types of meta-analytic approaches. [ABSTRACT FROM AUTHOR]

Categorical moderators are often included in mixed‐effects meta‐analysis to explain heterogeneity in effect sizes. An assumption in tests of categorical moderator effects is that of a constant between‐study variance across all levels of the moderator. Although it rarely receives serious thought, there can be statistical ramifications to upholding this assumption. We propose that researchers should instead default to assuming unequal between‐study variances when analysing categorical moderators. To achieve this, we suggest using a mixed‐effects location‐scale model (MELSM) to allow group‐specific estimates for the between‐study variance. In two extensive simulation studies, we show that in terms of Type I error and statistical power, little is lost by using the MELSM for moderator tests, but there can be serious costs when an equal variance mixed‐effects model (MEM) is used. Most notably, in scenarios with balanced sample sizes or equal between‐study variance, the Type I error and power rates are nearly identical between the MEM and the MELSM. On the other hand, with imbalanced sample sizes and unequal variances, the Type I error rate under the MEM can be grossly inflated or overly conservative, whereas the MELSM does comparatively well in controlling the Type I error across the majority of cases. A notable exception where the MELSM did not clearly outperform the MEM was in the case of few studies (e.g., 5). With respect to power, the MELSM had similar or higher power than the MEM in conditions where the latter produced non‐inflated Type 1 error rates. Together, our results support the idea that assuming unequal between‐study variances is preferred as a default strategy when testing categorical moderators. [ABSTRACT FROM AUTHOR]

In a systematic review of a diagnostic performance, summarizing performance metrics is crucial. There are various summary models in the literature, and hence model selection becomes inevitable. However, most existing large-sample-based model selection approaches may not fit in a meta-analysis of diagnostic studies, typically having a rather small sample size. Researchers need to effectively determine the final model for further inference, which motivates this article to investigate existing methods and to suggest a more robust method for this need. We considered models covering several widely-used methods for bivariate summary of sensitivity and specificity. Simulation studies were conducted based on different number of studies and different population sensitivity and specificity. Then final models were selected using several existing criteria, and we compared the summary receiver operating characteristic (sROC) curves to the theoretical ROC curve given the generating model. Even though parametric likelihood-based criteria are often applied in practice for their asymptotic property, they fail to consistently choose appropriate models under the limited number of studies. When the number of studies is as small as 10 or 5, our suggestion is best in different scenarios. An example for summary ROC curves for chemiluminescence immunoassay (CLIA) used in COVID-19 diagnosis is also illustrated. [ABSTRACT FROM AUTHOR]

Twin and family studies have historically aimed to partition phenotypic variance into components corresponding to additive genetic effects (A), common environment (C), and unique environment (E). Here we present the ACE Model and several extensions in the Adolescent Brain Cognitive Development℠ Study (ABCD Study®), employed using the new Fast Efficient Mixed Effects Analysis (FEMA) package. In the twin sub-sample (n = 924; 462 twin pairs), heritability estimates were similar to those reported by prior studies for height (twin heritability = 0.86) and cognition (twin heritability between 0.00 and 0.61), respectively. Incorporating SNP-derived genetic relatedness and using the full ABCD Study® sample (n = 9,742) led to narrower confidence intervals for all parameter estimates. By leveraging the sparse clustering method used by FEMA to handle genetic relatedness only for participants within families, we were able to take advantage of the diverse distribution of genetic relatedness within the ABCD Study® sample. [ABSTRACT FROM AUTHOR]

Although low-income homeownership programs serving vulnerable families at the lower end of the income distribution have been the focus of housing policy in many countries over the past 50 years, little is known about the post-origination experiences of immigrant families participating in these programs. Notably absent from the extant literature are studies examining the sustainability of homeownership among immigrant homebuyers and their susceptibility to falling behind on payments and experiencing mortgage defaults, evictions, or short sales. Utilizing data from 8263 families participating in Norway's Starter Mortgage Program (Startlån) during the first three calendar years after mortgage origination, we examine the extent to which serious mortgage arrears varies by immigrant background. Two primary questions shape our research: (1) What is the incidence of serious mortgage arrears among Western, Eastern European, and non-Western immigrant homeowners relative to ethnic Norwegians participating in a public low-income homeownership program? and (2) What pre- and post-origination characteristics of applicants and households, mortgage terms at the time of origination, and experiences of household financial vulnerability or economic shocks predict heterogeneity in serious mortgage arrears by immigrant backgrounds? We found that 6.1% of ethnic Norwegian, 6.2% of Western, 4.9% of non-Western, and 3.2% of Eastern European immigrant homeowners participating in the Starter Mortgage Program were in serious mortgage arrears at least once during the first three calendar years after mortgage origination. Results from our negative binomial regression analyses suggest that program participants who were sole owners, with larger families, and higher debt were more likely to experience serious mortgage arrears; these effects were accentuated for ethnic Norwegians. Additionally, mortgage terms at the time of origination produced differential effects by immigrant background. Compared to Western and Eastern European immigrant homeowners, ethnic Norwegians were more likely to have experienced serious mortgage arrears if they purchased a single-family home, had larger LTV and DTI ratios, or if the Startlån share of their mortgages was higher. Non-Western immigrant mortgagors were more likely to make late mortgage payments if they had larger LTV ratios, interest-only mortgage servicing, or if they were more reliant on Startlån funds to finance their mortgages; however, this risk was reduced if they had fixed-rate mortgages. Financial vulnerability in terms of higher debt or fewer assets also increased the risk of serious mortgage arrears for ethnic Norwegians and non-Western immigrant homeowners, while increases in real wealth reduced that risk for all immigrant mortgagor groups. [ABSTRACT FROM AUTHOR]

The growing push in nonprofit studies toward panel data necessitates a methodological guide tailored for nonprofit scholars and practitioners. Panel data analysis can be a robust tool in advancing the understanding of causal and/or more nuanced inferences that many nonprofit scholars seek. This study provides a walk-through of the assumptions and common modeling approaches in panel data analysis, as well as an empirical illustration of the models using data from the nonprofit housing sector. In addition, the paper compiles applications of panel data analysis by scholars in leading nonprofit journals for further reference. [ABSTRACT FROM AUTHOR]

We study the impact of peer effects on the academic achievement of economics students in Pontifical Catholic University of Ecuador (PUCE) Ecuador, for both semesters of 2018. The estimates from our random-effects model show a significant influence of the average-group, high-achieving, and low-achieving peers. These results are robust with the presence of socioeconomic, academic, and teaching quality covariates. The findings suggest that systems that prioritize course enrollment according to previous scores may exacerbate a peer-driven bias in student performance. [ABSTRACT FROM AUTHOR]

We consider Kolmogorov–Smirnov and Cramér–von-Mises type tests for testing central symmetry, exchangeability, and independence. In the standard case, the tests are intended for the application to independent and identically distributed data with unknown distribution. The tests are available for multivariate data and bootstrap procedures are suitable to obtain critical values. We discuss the applicability of the tests to random effects models, where the random effects are independent but not necessarily identically distributed and with possibly unknown distributions. Theoretical results show the adequacy of the tests in this situation. The quality of the tests in models with random effects is investigated by simulations. Empirical results obtained confirm the theoretical findings. A real data example illustrates the application. [ABSTRACT FROM AUTHOR]

We propose the unit gamma mixed regression model to deal with continuous bounded variables in the context of repeated measures and clustered data. The proposed model is based on the class of generalized linear mixed models and parameter estimates are obtained based on the maximum likelihood method. The computational implementation combines automatic differentiation and the Laplace approximation (via Template Model Builder/C++) to compute the derivatives of the log-likelihood function with respect to fixed and random effects parameters. We carry out extensive simulations to check the computational implementation and to verify the properties of the maximum likelihood estimators. Our results suggest that the proposed maximum likelihood approach provides unbiased and consistent estimators for all model parameters. The proposed model was motivated by two data sets. The first concerns the body fat percentage, where the goal was to investigate the effect of covariates which were taken in the same subject. The second data set refers to a water quality index data, where the main interest was to evaluate the effect of dams on the water quality measured on power plant reservoirs. The data sets and R code are provided as supplementary material. [ABSTRACT FROM AUTHOR]

Background: Technological advances involving RNA-Seq and Bioinformatics allow quantifying the transcriptional levels of genes in cells, tissues, and cell lines, permitting the identification of Differentially Expressed Genes (DEGs). DESeq2 and edgeR are well-established computational tools used for this purpose and they are based upon generalized linear models (GLMs) that consider only fixed effects in modeling. However, the inclusion of random effects reduces the risk of missing potential DEGs that may be essential in the context of the biological phenomenon under investigation. The generalized linear mixed models (GLMM) can be used to include both effects. Methods: We present DEGRE (Differentially Expressed Genes with Random Effects), a user-friendly tool capable of inferring DEGs where fixed and random effects on individuals are considered in the experimental design of RNA-Seq research. DEGRE preprocesses the raw matrices before fitting GLMMs on the genes and the derived regression coefficients are analyzed using the Wald statistical test. DEGRE offers the Benjamini-Hochberg or Bonferroni techniques for P-value adjustment. Results: The datasets used for DEGRE assessment were simulated with known identification of DEGs. These have fixed effects, and the random effects were estimated and inserted to measure the impact of experimental designs with high biological variability. For DEGs' inference, preprocessing effectively prepares the data and retains overdispersed genes. The biological coefficient of variation is inferred from the counting matrices to assess variability before and after the preprocessing. The DEGRE is computationally validated through its performance by the simulation of counting matrices, which have biological variability related to fixed and random effects. DEGRE also provides improved assessment measures for detecting DEGs in cases with higher biological variability. We show that the preprocessing established here effectively removes technical variation from those matrices. This tool also detects new potential candidate DEGs in the transcriptome data of patients with bipolar disorder, presenting a promising tool to detect more relevant genes. Conclusions: DEGRE provides data preprocessing and applies GLMMs for DEGs' inference. The preprocessing allows efficient remotion of genes that could impact the inference. Also, the computational and biological validation of DEGRE has shown to be promising in identifying possible DEGs in experiments derived from complex experimental designs. This tool may help handle random effects on individuals in the inference of DEGs and presents a potential for discovering new interesting DEGs for further biological investigation. [ABSTRACT FROM AUTHOR]

Under the Bayesian framework, this study proposes a Tweedie compound Poisson partial linear mixed model on the basis of Bayesian P-spline approximation to nonparametric function for longitudinal semicontinuous data in the presence of nonignorable missing covariates and responses. The logistic regression model is simultaneously used to specify the missing response and covariate mechanisms. A hybrid algorithm combining the Gibbs sampler and the Metropolis–Hastings algorithm is employed to produce the joint Bayesian estimates of unknown parameters and random effects as well as nonparametric function. Several simulation studies and a real example relating to the osteoarthritis initiative data are presented to illustrate the proposed methodologies. [ABSTRACT FROM AUTHOR]

Recent empirical studies have quantified correlation between survival and recovery by estimating these parameters as correlated random effects with hierarchical Bayesian multivariate models fit to tag‐recovery data. In these applications, increasingly negative correlation between survival and recovery has been interpreted as evidence for increasingly additive harvest mortality. The power of these hierarchal models to detect nonzero correlations has rarely been evaluated, and these few studies have not focused on tag‐recovery data, which is a common data type. We assessed the power of multivariate hierarchical models to detect negative correlation between annual survival and recovery. Using three priors for multivariate normal distributions, we fit hierarchical effects models to a mallard (Anas platyrhychos) tag‐recovery data set and to simulated data with sample sizes corresponding to different levels of monitoring intensity. We also demonstrate more robust summary statistics for tag‐recovery data sets than total individuals tagged. Different priors led to substantially different estimates of correlation from the mallard data. Our power analysis of simulated data indicated most prior distribution and sample size combinations could not estimate strongly negative correlation with useful precision or accuracy. Many correlation estimates spanned the available parameter space (−1,1) and underestimated the magnitude of negative correlation. Only one prior combined with our most intensive monitoring scenario provided reliable results. Underestimating the magnitude of correlation coincided with overestimating the variability of annual survival, but not annual recovery. The inadequacy of prior distributions and sample size combinations previously assumed adequate for obtaining robust inference from tag‐recovery data represents a concern in the application of Bayesian hierarchical models to tag‐recovery data. Our analysis approach provides a means for examining prior influence and sample size on hierarchical models fit to capture–recapture data while emphasizing transferability of results between empirical and simulation studies. [ABSTRACT FROM AUTHOR]

In grassland ecosystems, it is well known that increasing plant species diversity can improve ecosystem functions (i.e., ecosystem responses), for example, by increasing productivity and reducing weed invasion. Diversity-Interactions models use species proportions and their interactions as predictors in a regression framework to assess biodiversity and ecosystem function relationships. However, it can be difficult to model numerous interactions if there are many species, and interactions may be temporally variable or dependent on spatial planting patterns. We developed a new Diversity-Interactions mixed model for jointly assessing many species interactions and within-plot species planting pattern over multiple years. We model pairwise interactions using a small number of fixed parameters that incorporate spatial effects and supplement this by including all pairwise interaction variables as random effects, each constrained to have the same variance within each year. The random effects are indexed by pairs of species within plots rather than a plot-level factor as is typical in mixed models, and capture remaining variation due to pairwise species interactions parsimoniously. We apply our novel methodology to three years of weed invasion data from a 16-species grassland experiment that manipulated plant species diversity and spatial planting pattern and test its statistical properties in a simulation study.Supplementary materials accompanying this paper appear online. [ABSTRACT FROM AUTHOR]

Recognizing the increasing precedence of fiscal shocks leading to a deterioration in states' debt due to the realization of contingent liabilities, this study assesses the debt sustainability of Indian states by employing both conventional and augmented debts, obtained by incorporating information on states' guarantees. Results indicate that states' debt is just sustainable with potential signs of unsustainability. Guarantees given by states, if invoked, could certainly pose a potential risk to debt sustainability for Indian states. The study suggests revisiting and reviewing states' FRLs with the inclusion of debt as a medium-term anchor, and greater transparency with regard to contingent liabilities. [ABSTRACT FROM AUTHOR]

Purpose: Rural women in Africa have less entrepreneurship opportunities than men. This is mainly due to societal expectations of women, but it may also be caused by a lack of entrepreneurial knowledge and skills due to their work in agriculture-related activities. Therefore, the purpose of this study is to examine the impact of entrepreneurship training on entrepreneurial skillsets of rural women working in Uganda and how such training influences their entrepreneurial activity. Design/methodology/approach: The primary data set for this study comes from surveys of rural women working in agriculture who participated in a specific entrepreneurship training programme in Uganda. A panel data set is gathered from surveying 298 women before and after the training programme. A random effects regression method is utilised to estimate the impact of entrepreneurship training and other sociodemographic characteristics on entrepreneurial skillsets. Findings: The key finding of this study is that entrepreneurship training increases entrepreneurial skillsets by 25% and that further training and educational opportunities may improve social standing and living standards of rural women working in agriculture. Originality: This study offers distinctive insights into female entrepreneurship in Africa as it quantitatively examines the impact of entrepreneurship training on entrepreneurial skillsets of rural women in Uganda. The findings of this study may inform policymakers of the benefits of appropriate training programmes to improve the living standards, social standing, and economic outcomes for rural women in Africa. [ABSTRACT FROM AUTHOR]

The purpose of this paper was to introduce the nested design and its quantitative data analysis of variance and the SAS implementation. If one of the following two characteristics existed in a specific experimental study， a nested design could be considered to arrange the experiment. Firstly， there was a nested relationship between factors in natural attributes. Secondly， with professional knowledge as the basis， the impact of each factor on the quantitative observation results was divided into primary and secondary. The first feature mentioned above meant that the factors related to the subjects had the conditions for grouping and regrouping. The second feature mentioned above meant that the status of each factor was unequal. In the variance analysis of quantitative data， the calculation formulas of variable error mean square was required to use. Based on four examples and with the help of the SAS software， this paper implemented the univariate analysis of variance for the quantitative data of the nested design， and gave the detailed explanations for the output results of SAS software.

• A failure-dependence related stochastic crack growth modeling approach for competing cracking mode is proposed. • Copulas are used to model the dependent competing relationships among multiple fatigue fracture modes. • The fatigue crack growth is modeled based on a Wiener process, with unit-to-unit variability addressed by random effects. • A two-stage Bayesian inference method based on Hamiltonian Monte Carlo sampling is proposed to estimate model parameters. • The effectiveness of the proposed approach is proven through a real case study. Competing crack-induced fatigue fracture has been a typical failure mode especially in the very high cycle fatigue regime. However, the stochastic crack growth modeling related to failure-dependence has not been fully investigated. Here, a failure-dependence related stochastic crack growth modeling approach for competing cracking mode is proposed to address this issue. Firstly, copulas are used to model the dependent competing relationships among multiple fatigue fracture modes. The reliability analysis of multiple fatigue fracture modes is conducted from a copula perspective. Besides, the fatigue crack growth is modeled based on a nonlinear Wiener process, with unit-to-unit variability addressed by introducing random effects. Marginal reliability expressions are derived based on the Wiener process. Furthermore, a two-stage Bayesian inference method based on Hamiltonian Monte Carlo sampling is proposed to estimate model parameters. A Monte Carlo simulation study is conducted to validate the accuracy and robustness of the proposed inference method. Finally, the effectiveness of the proposed approach is proven through a real case study. It turns out that the ignorance of the competing relationships among multiple cracking modes leads to an underestimation of overall reliability. The accuracy of the model can be further improved with random effects considered. [ABSTRACT FROM AUTHOR]

Profiling or evaluation of health care providers involves the application of statistical models to compare each provider's performance with respect to a patient outcome, such as unplanned 30-day hospital readmission, adjusted for patient case-mix characteristics. The nationally adopted method is based on random effects (RE) hierarchical logistic regression models. Although RE models are sensible for modeling hierarchical data, novel high dimensional fixed effects (FE) models have been proposed which may be well-suited for the objective of identifying sub-standard performance. However, there are limited comparative studies. Thus, we examine their relative performance, including the impact of inadequate case-mix adjustment.

Abstract Objective The main objective of this study is to fit Log logistic-Gamma shared frailty model for the determinant of time to age at first marriage among women in Ethiopia. Methods The data set in this study were obtained from Demography and Health survey conducted in Ethiopia in 2016. In this study, we used Log logistic-Gamma shared frailty model to account for the loss of independence that arises from the clustering of women in region of Ethiopia. A total of 12,066 women aged 15–49 in Ethiopia were included in this study. Results Of all 12,066 women aged 15–49, 9466 (78.45%) were married and the median & mean age at first marriage for women living in Ethiopia were 17.2 years and 17.5 years respectively, while the minimum and maximum age at first marriage observed were 8 years and 49 years respectively. Conclusion The most significant contributing factors to delaying time to age at first marriage of women aged 15–49 in Ethiopia were increased education level of women, increased education level of the head, increased income, residing in urban and being followers of religion other than orthodox, catholic, protestant & Muslim. The heterogeneity of age at first marriage for women aged 15–49 among regions in Ethiopia was observed. The government of Ethiopia and the concerned bodies should revise the women's health policy and practice to reduce early marriage and give attention to women; illiterate, live in rural areas, and have illiterate and poor heads.

This study empirically examined the dynamic impact of foreign capital inflows and trade openness on output performance and national productivity in 31 selected countries in sub-Saharan Africa (SSA) between 1985 and 2018. The study employed random effects and fixed effects models to estimate the coefficients. However, the results from the two models portray similar behaviors. Both estimates revealed a significant relationship between output performance and the independent variables. This suggests that the macroeconomic variables examined are good explanatory variables for analyzing the determinants of output performance and national productivity in the SSA region. The study further found that foreign capital inflows, trade openness and inflation rate have a positive and significant influence on output performance and national productivity. In contrast, exchange rate and interest rate exhibited a negative and significant relationship with such output performance. This result implies that policymakers in SSA countries must formulate policies that can successfully ensure trade openness and promote foreign capital inflows so as to stimulate national productivity and boost output performance in the region. Therefore, it can be concluded that foreign capital inflows and trade openness affect the industrial sector in contributing to output performance and national productivity in the SSA countries.

Background Technological advances involving RNA-Seq and Bioinformatics allow quantifying the transcriptional levels of genes in cells, tissues, and cell lines, permitting the identification of Differentially Expressed Genes (DEGs). DESeq2 and edgeR are well-established computational tools used for this purpose and they are based upon generalized linear models (GLMs) that consider only fixed effects in modeling. However, the inclusion of random effects reduces the risk of missing potential DEGs that may be essential in the context of the biological phenomenon under investigation. The generalized linear mixed models (GLMM) can be used to include both effects. Methods We present DEGRE (Differentially Expressed Genes with Random Effects), a user-friendly tool capable of inferring DEGs where fixed and random effects on individuals are considered in the experimental design of RNA-Seq research. DEGRE preprocesses the raw matrices before fitting GLMMs on the genes and the derived regression coefficients are analyzed using the Wald statistical test. DEGRE offers the Benjamini-Hochberg or Bonferroni techniques for P-value adjustment. Results The datasets used for DEGRE assessment were simulated with known identification of DEGs. These have fixed effects, and the random effects were estimated and inserted to measure the impact of experimental designs with high biological variability. For DEGs’ inference, preprocessing effectively prepares the data and retains overdispersed genes. The biological coefficient of variation is inferred from the counting matrices to assess variability before and after the preprocessing. The DEGRE is computationally validated through its performance by the simulation of counting matrices, which have biological variability related to fixed and random effects. DEGRE also provides improved assessment measures for detecting DEGs in cases with higher biological variability. We show that the preprocessing established here effectively removes technical variation from those matrices. This tool also detects new potential candidate DEGs in the transcriptome data of patients with bipolar disorder, presenting a promising tool to detect more relevant genes. Conclusions DEGRE provides data preprocessing and applies GLMMs for DEGs’ inference. The preprocessing allows efficient remotion of genes that could impact the inference. Also, the computational and biological validation of DEGRE has shown to be promising in identifying possible DEGs in experiments derived from complex experimental designs. This tool may help handle random effects on individuals in the inference of DEGs and presents a potential for discovering new interesting DEGs for further biological investigation.

Mummolo and Peterson improve the use and interpretation of fixed-effects models by pointing out that unit intercepts fundamentally reduce the amount of variation of variables in fixed-effects models. Along a similar vein, we make two claims in the context of random effects models. First, we show that potentially large reductions in variation, in this case caused by quasi-demeaning, also occur in models using random effects. Second, in many instances, what authors claim to be a random effects model is actually a pooled model after the quasi-demeaning process, affecting how we should interpret the model. A literature review of random effects models in top journals suggests that both points are currently not well understood. To better help users interested in improving their interpretation of random effects models, we provide Stata and R programs to easily obtain post-estimation quasi-demeaned variables. [ABSTRACT FROM AUTHOR]

Purpose: Poverty is endemic in rural Nigeria and gender disparity in access to productive resources is a major cause of poverty in the area. Poverty status of rural households along gender line was, therefore, investigated in this study. Design/methodology/approach: Panel data from 2010/2011 (wave 1) and 2015/2016 (wave 3) of the Living Standard Measurement Survey (LSMS) for Nigeria were used for the study. Data were analysed using Foster, Greer and Thorbecke (FGT) poverty indices and binomial panel logistic model. Findings: Poverty measures for women-led households in farming activities were 58.5, 27.8 and 17.1%; men-led farming households had 59.8, 27.4 and 16.3% gender-neutral had 56.8%, 27 and 16.9% in the first panel. Poverty indices increased in the women-led and men-led farming households in the second panel. Poverty incidence was higher amongst farming households than the non-farming counterparts. Correlates of poverty status differ amongst the gender-groups were household size, farming, tertiary education, access to credit and geographical locations. Originality/value: Gender disparity is perceived in this study along the line of differences in gender composition of rural households. A gender-blind approach to poverty alleviation programmes likely will not enhance reduced poverty in rural Nigeria. Closing the gender poverty gap will ensure achievement of the first sustainable development goal of poverty eradication. [ABSTRACT FROM AUTHOR]

The aim of this research is to examine the impact of ESG (environmental, social, and governance factors) on the financial performance. In this study, the measurement of financial performance is undertaken by using accounting-based analysis including return on assets (ROA) and return on equity (ROE). The sample included 54 non-financial firms that are listed on the PSX and the data is collected from different sources including financial annual reports of the firms, World Bank and other sources for the period of 2010-2020. The estimation results following the Random Effects estimation methodology show that social factor, environmental factor and governance factor are having significant impact on ROA. Similarly, the estimation results show that social factor, environmental factor and governance factor were also having significant impact on ROE. The outcomes of the current study are also helpful to the management and policy makers. The study recommended that policy maker must manage their resources and invest in ESG activities for uplifting their financial performance in the long run. [ABSTRACT FROM AUTHOR]

Objective: Random effects are often neglected when defining the control strategy for a biopharmaceutical process. In this article, we present a case study that highlights the importance of considering the variance introduced by random effects in the calculation of proven acceptable ranges (PAR), which form the basis of the control strategy. Methods: Linear mixed models were used to model relations between process parameters and critical quality attributes in a set of unit operations that comprises a typical biopharmaceutical manufacturing process. Fitting such models yields estimates of fixed and random effect sizes as well as random and residual variance components. To form PARs, tolerance intervals specific to mixed models were applied that incorporate the random effect contribution to variance. Results: We compared standardized fixed and random effect sizes for each unit operation and CQA. The results show that the investigated random effect is not only significant but in some unit operations even larger than the average fixed effect. A comparison between ordinary least squares and mixed models tolerance intervals shows that neglecting the contribution of the random effect can result in PARs that are too optimistic. Conclusions: Uncontrollable effects such as week-to-week variability play a major role in process variability and can be modelled as a random effect. Following a workflow such as the one suggested in this article, random effects can be incorporated into a statistically sound control strategy, leading to lowered out of specification results and reduced patient risk. [ABSTRACT FROM AUTHOR]

The traffic flow pertinent modelling is essential for distinct strategy formulations. However, the present literature illustrated some needed improvements for such models, especially those related to lane-specific flow. Given such context, this study aims to bridge the research gap by generating regression models to investigate influential factors for traffic flow based on the data collected from one multilane freeway. With the Full Bayesian specification, the hierarchical models were built by accounting for three types of random effects: the structured and unstructured spatial effects, and the one addressing the multivariate heterogeneity across multiple lanes. The endogenous relationship of traffic flow of adjacent lanes was also explored by utilizing the capability of multivariate correlation structure for simultaneous estimation of lane flow. The model estimates revealed the presence of endogeneity with statistical significance for the flow of neighbouring lanes for both directions of travel. The impact of flow was not only limited to the adjacent lanes but also to non-adjacent lanes. The multivariate specification also confirmed interdependency for lane flows. Compared to conventional approaches, the more accurate model estimation in the study indicates the advantage of incorporating the various correlation structures in the models. [ABSTRACT FROM AUTHOR]

Featured Application: This manuscript is part of ongoing research on the strengths and limitations of Wald statistical tests taking into account the incorrect specification of distribution of random effects in generalized linear mixed models. Generalized linear mixed models are commonly used in repeated measurement studies and account for the dependence between observations obtained from the same experimental unit. The designs of repeated measurements in which each experimental unit (e.g., subject) is proven in more than one experimental condition are widespread in psychology, neuroscience, medicine, social sciences and agricultural research. Estimation in generalized linear mixed models is often based on the maximum likelihood theory, which assumes that the assumptions about the underlying probability model are correct. These assumptions include the specification of the distribution of random effects. This research study aimed to identify the impact of the incorrect specification of this distribution on the probability of a type I error and the statistical power of the Wald test. This was achieved through a simulation study where different distributions were considered for random effects in generalized linear mixed models with Poisson and negative binomial responses. Evidence of the impact of the incorrect specification was presented in distributions for random effects different from the normal ones. Lognormal was used for random intercepts and bivariate exponential and Tukey for random intercepts and slopes. Lognormal has positive asymmetry and high kurtosis. Exponential has moderate asymmetry and kurtosis, and Tukey has moderate asymmetry and high kurtosis. [ABSTRACT FROM AUTHOR]

Detecting the abnormal degradation rate of Wiener processes is important for quality control and reliability management. In this paper, we propose upper-side CUSUM control schemes for monitoring the abnormal degradation rate of three classes of Wiener processes. Average run length and standard deviation run length are applied to evaluating the efficiency of CUSUM control schemes. The results show that the proposed CUSUM control schemes can efficiently give out-of-control signals when the degradation rate of Wiener process accelerates. It is also shown that initial values of degradation rate and sampling frequency have significant influence on the performance of CUSUM control schemes. [ABSTRACT FROM AUTHOR]

Assessing disease comorbidity patterns in families represents the first step in gene mapping for diseases and is central to the practice of precision medicine. One way to evaluate the relative contributions of genetic risk factor and environmental determinants of a complex trait (e.g. Alzheimer's disease [AD]) and its comorbidities (e.g. cardiovascular diseases [CVD]) is through familial studies, where an initial cohort of subjects are recruited, genotyped for specific loci, and interviewed to provide extensive disease history in family members. Because of the retrospective nature of obtaining disease phenotypes in family members, the exact time of disease onset may not be available such that current status data or interval-censored data are observed. All existing methods for analyzing these family study data assume single event subject to right-censoring so are not applicable. In this article, we propose a semiparametric regression model for the family history data that assumes a family-specific random effect and individual random effects to account for the dependence due to shared environmental exposures and unobserved genetic relatedness, respectively. To incorporate multiple events, we jointly model the onset of the primary disease of interest and a secondary disease outcome that is subject to interval-censoring. We propose nonparametric maximum likelihood estimation and develop a stable Expectation-Maximization (EM) algorithm for computation. We establish the asymptotic properties of the resulting estimators and examine the performance of the proposed methods through simulation studies. Our application to a real world study reveals that the main contribution of comorbidity between AD and CVD is due to genetic factors instead of environmental factors. [ABSTRACT FROM AUTHOR]