6,757 Results Found

Linear mixed-effects models are widely used in applications to analyze clustered, hierarchical, and longitudinal data. Model selection in linear mixed models is more challenging than that of linear models as the parameter vector in a linear mixed mod...

Data analysis using the General linear model assumes the factors to be fixed effects, and the BLUE method, which is based on their mean performance, is appropriate to select the best performing genotypes. The linear mixed model incorporates fixed and...

The classical model only provides a correct analysis if all the effects are fixed. For experiments that include fixed and random effects, the general linear mixed model is appropriate for handling the non-normal distributed response variables. The ai...

In the application of control charts, most of the research in profile monitoring is based on accurate measurements. Measurement errors, however, often exist in many manufacturing and service environments. In this paper, we apply linear mixed models i...

In applying a hierarchical mixed model to genome-wide association analysis (GWAS) of longitudinal data, dimensionality reduction through modeling repeated measurements improves both computational efficiency and statistical power. Legendre polynomials...

The Asian Emerald Ash Borer beetle (EAB, Agrilus planipennis Fairmaire) can cause damage to all species of Ash trees (Fraxinus), and rampant, unchecked infestations of this insect can cause significant damage to forests. It is thus critical to assess...

This work considers a multifactor linear mixed model under heteroscedasticity in random-effect factors and the skew-normal errors for modeling the correlated datasets. We implement an expectation–maximization (EM) algorithm to achieve the maxim...

Data breach incidents result in severe financial loss and reputational damage, which raises the importance of using insurance to manage and mitigate cyber related risks. We analyze data breach chronology collected by Privacy Rights Clearinghouse (PRC...

Many parts of big data, such as web documents, online posts, papers, patents, and articles, are in text form. So, the analysis of text data in the big data domain is an important task. Many methods based on statistics or machine learning algorithms h...

In this study, four major muscles acting on the scapula were investigated in patients who had been treated in the last six years for unilateral carcinoma of the breast. Muscle activity was assessed by electromyography during abduction and adduction o...

This paper deals with symmetrical data that can be modelled based on Gaussian distribution, such as linear mixed models for longitudinal data. The latent factor linear mixed model (LFLMM) is a method generally used for analysing changes in high-dimen...

The purpose of this paper is to propose a new algorithm based on stochastic expectation maximization (SEM) to deal with the problem of unobserved values when multiple interactions in a linear mixed-effects model (LMEM) are present. We test the effect...

Chub mackerel (Scomber japonicus) is a commercially important fish species which are widely distributed in the North Pacific. Based on the fishery data from China’s high-sea light-purse seine fishing from 2014 to 2020 and the marine environment...

Tensor-structured multi-scale longitudinal data are quite prevalent in epidemiological, clinical, and other follow-up research. A key challenge in their statistical analysis is to simultaneously realize dimension reduction and longitudinal correlatio...

Change-point models are frequently considered when modeling phenomena where a regime shift occurs at an unknown time. In aging research, these models are commonly adopted to estimate of the onset of cognitive decline. Yet these models present several...

This work proposes a mixed-integer linear programming model for the operational cost function of lithium-ion batteries that should be applied in a microgrid centralized controller. Such a controller aims to supply loads while optimizing the leveled c...

The estimation of large functional and longitudinal data, which refers to the estimation of mean function, estimation of covariance function, and prediction of individual trajectory, is one of the most challenging problems in the field of high-dimens...

Analysis of longitudinal data in high-dimensional gene–environment interaction studies have been extensively conducted using variable selection methods. Despite their success, these studies have been consistently challenged by the lack of uncer...

Linear mixed models (LMMs) are commonly used in genome-wide association studies (GWASs) to evaluate population structures and relatedness. However, LMMs have been shown to be ineffective in controlling false positive errors for the analysis of resist...

Aerosol optical depth (AOD), an important indicator of atmospheric aerosol load, characterizes the impacts of aerosol on radiation balance and atmospheric turbidity. The nesting Elterman model and a spatiotemporal linear mixed-effects (ST-LME) model,...

Data imbalance is common in multi-environment trials (METs) and complicates genotype and environment evaluations. To address this, linear mixed models (LMMs) and factor-analytic linear mixed models (FA-LMMs) were employed to estimate best linear unbi...

The accurate estimation of aboveground biomass (AGB) and leaf area index (LAI) is critical to characterize crop growth status and predict grain yield. Unmanned aerial vehicle (UAV) -based remote sensing has attracted significant interest due to its h...

The installation of offshore wind farms poses particular challenges due to expensive resources and quickly changing weather conditions. Model-based decision-support systems are required to achieve an efficient installation. In the literature, there e...

Quantifying the benefits of construction supply chain management through prescriptive models is a challenging and fast-growing research area that still lacks standardized optimization models with full integrative potential. In response to the needs a...

Recently, financial inclusion and bank stability have gained attention among researchers, particularly since the 2008 global financial crisis. This study investigates how financial inclusion may have influenced bank stability given differences in ban...

The purpose of this study is to investigate the effects of toolpath patterns, geometry types, and layering effects on the mechanical properties of parts manufactured by direct energy deposition (DED) additive manufacturing using data analysis and mac...

We study a novel Doubly penalized ERror Function regularized Quantile Regression (DERF-QR) in this paper. This is a method of variable selection by ERror Function (ERF) regularization in the linear effects model. We introduce a two-stage iterative al...

Fusarium oxysporum exhibits insect pathogenicity—however, generalized concerns of releasing phytopathogens within agroecosystems marred its entomopathogenicity-related investigations. In a previous study, soils were sampled from Douro vineyards...

The development of a linear mixed model to describe the degradation of friction on flexible road pavements to be included in pavement management systems is the aim of this study. It also aims at showing that, at the network level, factors such as tem...

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....

Spectral unmixing is one of the prime topics in hyperspectral image analysis, as images often contain multiple sources of spectra. Spectral variability is one of the key factors affecting unmixing accuracy, since spectral signatures are affected by v...

This paper studies a heteroscedastic partially linear model based on ${\rho }^{-}$ -mixing random errors, stochastically dominated and with zero mean. Under some suitable conditions, the strong consistency and $p$ -th $\left(p>0\right)$...

Longitudinal studies routinely generate large-scale multivariate data in which multiple responses are measured repeatedly for each subject over time. Fitting the popular class of linear mixed-effects models to such data and obtaining maximum likeliho...

Gene-environment (GE) interaction has important implications in the etiology of complex diseases that are caused by a combination of genetic factors and environment variables. Several authors have developed GE analysis in the context of independent s...

In high-dimensional regression models, the Bayesian lasso with the Gaussian spike and slab priors is widely adopted to select variables and estimate unknown parameters. However, it involves large matrix computations in a standard Gibbs sampler. To so...

In a host of business applications, biomedical and epidemiological studies, the problem of multicollinearity among predictor variables is a frequent issue in longitudinal data analysis for linear mixed models (LMM). We consider an efficient estimatio...

The opioid crisis in the United States poses a major threat to public health due to psychiatric and infectious disease comorbidities and death due to opioid use disorder (OUD). OUD is characterized by patterns of opioid misuse leading to persistent h...

The main goal of this paper is to propose a two-step method for the estimation of parameters in non-linear mixed-effects models. A first-step estimate $\tilde{\theta }$ of the vector $\theta$ of parameters is obtained by solving estimation equations, wit...

Linear mixed-effects modeling has been used to characterize fish growth changes over time, but early studies did not fully consider the implicit relationship among the three major factors of fish age, year-class, and sampling year in model developmen...

Surface texture plays a key role in pavement safety and performance, yet its degradation is influenced by multiple interacting factors that vary across road networks. This study developed statistical models to characterize and predict surface texture...

Crown profile is mostly related to the competition of individual trees in the stands, light interception, growth, and yield of trees. A total of 76 sample trees with a total number of 889 whorls and 3658 live branches were used to develop the outer c...

Much recent research focuses on how to make disease detection more accurate as well as “slimmer”, i.e., allowing analysis with smaller datasets. Explanatory models are a hot research topic because they explain how the data are generated....

Enhancing model interpretability remains an ongoing challenge in predictive modelling, especially when applied to simulation data from complex systems. Investigating the influence and effects of design factors within computer simulations of complex s...

This paper constructs the double penalized expectile regression for linear mixed effects model, which can estimate coefficient and choose variable for random and fixed effects simultaneously. The method based on the linear mixed effects model by cojo...

Genome-wide association study (GWAS) is the most popular approach to dissecting complex traits in plants, humans, and animals. Numerous methods and tools have been proposed to discover the causal variants for GWAS data analysis. Among them, linear mi...

Neurophysiological studies are often designed to examine relationships between measures from different testing conditions, time points, or analysis techniques within the same group of participants. Appropriate statistical techniques that can take int...

Land use influences water quality in streams at different spatial scales and varies in time and space. Water quality has long been associated with agricultural and urban land uses in catchments. The effects of developed, forest, pasture, and agricult...

Linear Mixed Effect (LME) models are applied to the CoastColour atmospherically-corrected Medium Resolution Imaging Spectrometer (MERIS) reflectance, L2R full resolution product, to derive chlorophyll-a (chl-a) concentration and Secchi disk depth (SD...

Integrating behavioral and physiological assessment is critical to improve our ability to assess animal welfare in biomedical settings. Hair, blood, and saliva samples were collected from 40 recently acquired male African green monkeys (AGMs) to anal...

Territory risk analysis has played an important role in auto insurance rate regulation. It aims to design rating territories from a set of basic rating units so that their respective risk relativities can be estimated to reflect the regional risk of...