Showing papers on "Random effects model published in 2021"
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28 Apr 2021
TL;DR: In this article, the authors proposed a two-way error component regression model for estimating the likelihood of a particular item in a set of data points in a single-dimensional graph.
Abstract: Preface.1. Introduction.1.1 Panel Data: Some Examples.1.2 Why Should We Use Panel Data? Their Benefits and Limitations.Note.2. The One-way Error Component Regression Model.2.1 Introduction.2.2 The Fixed Effects Model.2.3 The Random Effects Model.2.4 Maximum Likelihood Estimation.2.5 Prediction.2.6 Examples.2.7 Selected Applications.2.8 Computational Note.Notes.Problems.3. The Two-way Error Component Regression Model.3.1 Introduction.3.2 The Fixed Effects Model.3.3 The Random Effects Model.3.4 Maximum Likelihood Estimation.3.5 Prediction.3.6 Examples.3.7 Selected Applications.Notes.Problems.4. Test of Hypotheses with Panel Data.4.1 Tests for Poolability of the Data.4.2 Tests for Individual and Time Effects.4.3 Hausman's Specification Test.4.4 Further Reading.Notes.Problems.5. Heteroskedasticity and Serial Correlation in the Error Component Model.5.1 Heteroskedasticity.5.2 Serial Correlation.Notes.Problems.6. Seemingly Unrelated Regressions with Error Components.6.1 The One-way Model.6.2 The Two-way Model.6.3 Applications and Extensions.Problems.7. Simultaneous Equations with Error Components.7.1 Single Equation Estimation.7.2 Empirical Example: Crime in North Carolina.7.3 System Estimation.7.4 The Hausman and Taylor Estimator.7.5 Empirical Example: Earnings Equation Using PSID Data.7.6 Extensions.Notes.Problems.8. Dynamic Panel Data Models.8.1 Introduction.8.2 The Arellano and Bond Estimator.8.3 The Arellano and Bover Estimator.8.4 The Ahn and Schmidt Moment Conditions.8.5 The Blundell and Bond System GMM Estimator.8.6 The Keane and Runkle Estimator.8.7 Further Developments.8.8 Empirical Example: Dynamic Demand for Cigarettes.8.9 Further Reading.Notes.Problems.9. Unbalanced Panel Data Models.9.1 Introduction.9.2 The Unbalanced One-way Error Component Model.9.3 Empirical Example: Hedonic Housing.9.4 The Unbalanced Two-way Error Component Model.9.5 Testing for Individual and Time Effects Using Unbalanced Panel Data.9.6 The Unbalanced Nested Error Component Model.Notes.Problems.10. Special Topics.10.1 Measurement Error and Panel Data.10.2 Rotating Panels.10.3 Pseudo-panels.10.4 Alternative Methods of Pooling Time Series of Cross-section Data.10.5 Spatial Panels.10.6 Short-run vs Long-run Estimates in Pooled Models.10.7 Heterogeneous Panels.Notes.Problems.11. Limited Dependent Variables and Panel Data.11.1 Fixed and Random Logit and Probit Models.11.2 Simulation Estimation of Limited Dependent Variable Models with Panel Data.11.3 Dynamic Panel Data Limited Dependent Variable Models.11.4 Selection Bias in Panel Data.11.5 Censored and Truncated Panel Data Models.11.6 Empirical Applications.11.7 Empirical Example: Nurses' Labor Supply.11.8 Further Reading.Notes.Problems.12. Nonstationary Panels.12.1 Introduction.12.2 Panel Unit Roots Tests Assuming Cross-sectional Independence.12.3 Panel Unit Roots Tests Allowing for Cross-sectional Dependence.12.4 Spurious Regression in Panel Data.12.5 Panel Cointegration Tests.12.6 Estimation and Inference in Panel Cointegration Models.12.7 Empirical Example: Purchasing Power Parity.12.8 Further Reading.Notes.Problems.References.Index.
10,363 citations
TL;DR: In this article, the authors relax the assumption that the random effects and model errors follow a skew-normal distribution, which includes normality as a special case and provides flexibility in capturing a broad range of non-normal behavior.
Abstract: Normality (symmetric) of the random effects and the within-subject errors is a routine assumptions for the linear mixed model, but it may be unrealistic, obscuring important features of among- and within-subjects variation. We relax this assumption by considering that the random effects and model errors follow a skew-normal distributions, which includes normality as a special case and provides flexibility in capturing a broad range of non-normal behavior. The marginal distribution for the observed quantity is derived which is expressed in closed form, so inference may be carried out using existing statistical software and standard optimization techniques. We also implement an EM type algorithm which seem to provide some advantages over a direct maximization of the likelihood. Results of simulation studies and applications to real data sets are reported.
193 citations
TL;DR: The present review provides sufficient evidence for the estimate of serial interval of COVID-19, which can help in understanding the epidemiology and transmission of the disease.
90 citations
TL;DR: This work reviews 204 randomly drawn articles from macro and micro organizational science and applied psychology journals, finding that only 106 articles properly deal with the random effects assumption, and offers a set of practical recommendations for researchers to model multilevel data appropriately.
Abstract: Entities such as individuals, teams, or organizations can vary systematically from one another. Researchers typically model such data using multilevel models, assuming that the random effects are u...
89 citations
TL;DR: In this paper, a generalized linear mixed model with a random effect correction for individual as a means of accounting for within-sample correlation is proposed to compute differential expression within a specific cell type across treatment groups, to properly account for both zero inflation and the correlation structure among measures from cells within an individual.
Abstract: Cells from the same individual share common genetic and environmental backgrounds and are not statistically independent; therefore, they are subsamples or pseudoreplicates Thus, single-cell data have a hierarchical structure that many current single-cell methods do not address, leading to biased inference, highly inflated type 1 error rates, and reduced robustness and reproducibility This includes methods that use a batch effect correction for individual as a means of accounting for within-sample correlation Here, we document this dependence across a range of cell types and show that pseudo-bulk aggregation methods are conservative and underpowered relative to mixed models To compute differential expression within a specific cell type across treatment groups, we propose applying generalized linear mixed models with a random effect for individual, to properly account for both zero inflation and the correlation structure among measures from cells within an individual Finally, we provide power estimates across a range of experimental conditions to assist researchers in designing appropriately powered studies Single cell genomics uses cells from the same individual, or pseudoreplicates, that can introduce biases and inflate type I error rates Here the authors apply generalized linear mixed models with a random effect for individual, to properly account for both zero inflation and the correlation structure among cells within an individual
81 citations
TL;DR: In this article, a random effects estimator was proposed for binary choice panel data, where the probability of the outcomes of several individuals depend on the correlation of the unobserved heterogeneity.
Abstract: In a binary choice panel data framework, probabilities of the outcomes of several individuals depend on the correlation of the unobserved heterogeneity. I propose a random effects estimator that mo...
77 citations
TL;DR: PartR2 as discussed by the authors is a package that quantifies part R 2 for fixed effect predictors based on (generalized) linear mixed-effect model fits, which iteratively removes predictors of interest from the model and monitors the change in the variance of the linear predictor.
Abstract: The coefficient of determination R 2 quantifies the amount of variance explained by regression coefficients in a linear model. It can be seen as the fixed-effects complement to the repeatability R (intra-class correlation) for the variance explained by random effects and thus as a tool for variance decomposition. The R 2 of a model can be further partitioned into the variance explained by a particular predictor or a combination of predictors using semi-partial (part) R 2 and structure coefficients, but this is rarely done due to a lack of software implementing these statistics. Here, we introduce partR2, an R package that quantifies part R 2 for fixed effect predictors based on (generalized) linear mixed-effect model fits. The package iteratively removes predictors of interest from the model and monitors the change in the variance of the linear predictor. The difference to the full model gives a measure of the amount of variance explained uniquely by a particular predictor or a set of predictors. partR2 also estimates structure coefficients as the correlation between a predictor and fitted values, which provide an estimate of the total contribution of a fixed effect to the overall prediction, independent of other predictors. Structure coefficients can be converted to the total variance explained by a predictor, here called 'inclusive' R 2, as the square of the structure coefficients times total R 2. Furthermore, the package reports beta weights (standardized regression coefficients). Finally, partR2 implements parametric bootstrapping to quantify confidence intervals for each estimate. We illustrate the use of partR2 with real example datasets for Gaussian and binomial GLMMs and discuss interactions, which pose a specific challenge for partitioning the explained variance among predictors.
68 citations
23 Mar 2021
TL;DR: This Tutorial explains how to simulate data with random-effects structure and analyze the data using linear mixed-effects regression (with the lme4 R package), with a focus on interpreting the output in light of the simulated parameters.
Abstract: Experimental designs that sample both subjects and stimuli from a larger population need to account for random effects of both subjects and stimuli using mixed-effects models. However, much of this...
46 citations
TL;DR: GCA is a common large vessel vasculitis in those over age 50 years as mentioned in this paper, and the geographical and temporal distribution of the incidence, prevalence, and mortality of GCA is examined.
Abstract: Giant cell arteritis (GCA) is a common large vessel vasculitis in those over age 50 years This meta-analysis examined the geographical and temporal distribution of the incidence, prevalence, and mortality of GCA A systematic review was conducted using EMBASE, Scopus, and PubMed from their inceptions until 2019 Studies were included if they reported at least 50 or more GCA patients and defined the location and time frame Articles on mortality were included and standardized mortality ratio (SMR) was extracted where possible Mean pooled prevalence, incidence, and SMR were calculated using a random effects model Linear regression was used to explore correlations between latitude and incidence, prevalence, and mortality Of the 3569 citations identified, 107 were included The pooled incidence of GCA was 1000 [922, 1078] cases per 100,000 people over 50 years old This incidence was highest in Scandinavia 2157 [1890, 2423], followed by North and South America 1089 [878, 1300], Europe 726 [605, 847], and Oceania 785 [− 148, 1719] Pooled prevalence was 5174 [4204, 6143] cases per 100,000 people over age 50 Annual mortality was 2044 [1784, 2303] deaths/1000 Mortality generally decreased over the years of publication (p = 00008) Latitude correlated significantly with incidence (p = 00011), but not with prevalence, or mortality GCA incidence varies nearly 3-fold between regions and is highest in Scandinavia but not significantly Mortality may be improving over time
45 citations
TL;DR: This work exploits several estimators from both static panel and dynamic panel data models to show that the GMM estimator is more adequate than the traditional estimators based on fixed or random effects.
43 citations
TL;DR: In this article, the authors develop likelihood-based estimators for autoregressive panel data models that are consistent in the presence of time series heteroskedasticity, and investigate identification under unit roots, and show that random effects estimation in levels may achieve substantial efficiency gains relative to estimation from data in differences.
TL;DR: In this article, a consensus on how to specify a weakly informative heterogeneity prior is provided. But this consensus is limited to only few studies contributing to the meta-analysis, and it requires the sensible specification of prior distributions.
Abstract: The normal-normal hierarchical model (NNHM) constitutes a simple and widely used framework for meta-analysis. In the common case of only few studies contributing to the meta-analysis, standard approaches to inference tend to perform poorly, and Bayesian meta-analysis has been suggested as a potential solution. The Bayesian approach, however, requires the sensible specification of prior distributions. While noninformative priors are commonly used for the overall mean effect, the use of weakly informative priors has been suggested for the heterogeneity parameter, in particular in the setting of (very) few studies. To date, however, a consensus on how to generally specify a weakly informative heterogeneity prior is lacking. Here we investigate the problem more closely and provide some guidance on prior specification.
TL;DR: This work considers ME models where the random effects component is linear, and shows how these models can be improved on the basis of prior work on similar models.
Abstract: Mixed effects (ME) models inform a vast array of problems in the physical and social sciences, and are pervasive in meta-analysis. We consider ME models where the random effects component is linear...
TL;DR: It was revealed that the probability of critical crashes varies at any given value of real-time traffic-related predictors according to different combinations of longitudinal grade and road surface conditions, indicating an essential need for Active Traffic Management to timely apply interventions not only based on real- time traffic- related predictors but also according to various combinations of environmental conditions.
TL;DR: In this article, the authors developed a prevalence model taking account of survey and census data to de- rive small area prevalence estimates for diabetes in 32000 small area subdivisions (zip code census tracts) of the US.
Abstract: Information regarding small area prevalence of chronic disease is important for public health strategy and resourcing equity. This paper develops a prevalence model taking account of survey and census data to de- rive small area prevalence estimates for diabetes. The application involves 32000 small area subdivisions (zip code census tracts) of the US, with the prevalence estimates taking account of information from the US-wide Be- havioral Risk Factor Surveillance System (BRFSS) survey on population prevalence differentials by age, gender, ethnic group and education. The effects of such aspects of population composition on prevalence are widely recognized. However, the model also incorporates spatial or contextual in- fluences via spatially structured effects for each US state; such contextual effects are allowed to differ between ethnic groups and other demographic categories using a multivariate spatial prior. A Bayesian estimation ap- proach is used and analysis demonstrates the considerably improved fit of a fully specified compositional-contextual model as compared to simpler 'stan- dard' approaches which are typically limited to age and area effects.
TL;DR: In this article, the EKC hypothesis holds for the entire sample of 48 African countries, even though the relationship is weak, and there exist significant direct and spillover effects in the Co2-growth nexus across countries.
TL;DR: In this article, the authors consider the problem of spatial linear mixed-models, consisting of a linear covariate effect and a Gaussian process distributed spatial random effect, for analysis of geospatial data.
Abstract: Spatial linear mixed-models, consisting of a linear covariate effect and a Gaussian process (GP) distributed spatial random effect, are widely used for analyses of geospatial data. We consider the ...
TL;DR: A generalized p-value procedure is proposed to test whether there exist some heterogeneities among the degradation processes of different units and it is found that the performance of the GCI procedures is better than the Wald CIs and bootstrap-p CIs in terms of coverage probabilities.
TL;DR: In this paper, the R package cAIC4 is introduced that allows for the computation of the conditional Akaike information criterion (cAIC), which takes into account the uncertainty of the random effects variance and is therefore not straightforward.
Abstract: Model selection in mixed models based on the conditional distribution is appropriate for many practical applications and has been a focus of recent statistical research. In this paper we introduce the R package cAIC4 that allows for the computation of the conditional Akaike information criterion (cAIC). Computation of the conditional AIC needs to take into account the uncertainty of the random effects variance and is therefore not straightforward. We introduce a fast and stable implementation for the calculation of the cAIC for (generalized) linear mixed models estimated with lme4 and (generalized) additive mixed models estimated with gamm4. Furthermore, cAIC4 offers a stepwise function that allows for an automated stepwise selection scheme for mixed models based on the cAIC. Examples of many possible applications are presented to illustrate the practical impact and easy handling of the package.
TL;DR: The Woods Hole Assessment Model (WHAM) as mentioned in this paper is a software package that combines two approaches to estimate time and age-varying random effects on annual transitions in numbers at age (NAA), M, and selectivity, as well as fit environmental time-series with process and observation errors, missing data, and nonlinear links to R and M.
TL;DR: Wang et al. as mentioned in this paper developed a mixed effect model (MEM) to estimate the ground-level NO2 in China from January 1, 2014 to June 30, 2020 and other multivariate auxiliary data such as meteorological elements and terrain elevation.
TL;DR: The empirical results uncover that the electricity energy consumption can be best explained by population in combination with both the nighttime light intensity and tweet volume and the domestic electricity energy Consumption can be better explained than its non-domestic counterpart.
TL;DR: This article uses statistical theory and simulated data to show that EB estimates are biased toward zero, a phenomenon known as “shrinkage,” and illustrates these issues using an empirical data set on emotion regulation and neuroticism.
Abstract: Empirical Bayes (EB) estimates of the random effects in multilevel models represent how individuals deviate from the population averages and are often extracted to detect outliers or used as predictors in follow-up analysis. However, little research has examined whether EB estimates are indeed reliable and valid measures of individual traits. In this article, we use statistical theory and simulated data to show that EB estimates are biased toward zero, a phenomenon known as "shrinkage." The degree of shrinkage and reliability of EB estimates depend on a number of factors, including Level-1 residual variance, Level-1 predictor variance, Level-2 random effects variance, and number of within-person observations. As a result, EB estimates may not be ideal for detecting outliers, and they produce biased regression coefficients when used as predictors. We illustrate these issues using an empirical data set on emotion regulation and neuroticism.
TL;DR: In this article, a simple bias correction for linear dynamic panel data models is proposed and its asymptotic properties are studied when the number of time periods is fixed or tends to infinity with a number of panel units, and it can accommodate both fixed-effects and random effects assumptions, heteroskedastic errors, as well as higher-order autoregressive models.
TL;DR: The SCS estimator outperforms the bivariate model estimator and thus represents an improvement in the approach to diagnostic meta-analyses.
TL;DR: In a meta-analysis, a question always arises: is it worthwhile to combine estimates from studies of different populations using various formulations of an intervention, evaluating outcomes measured differently? Sometimes even study designs differ.
TL;DR: In this paper, a systematic review and meta-analysis was conducted to estimate the effects of overweight and obesity on COVID-19 disease severity, which showed that overweight and higher BMI is one of the leading comorbidities to increase the risk of COVID19 severity.
TL;DR: In this article, a Bayesian approach to combine survival estimates based on posterior predictive stacking, where the weights are formed to maximize posterior predictive accuracy, is proposed to estimate allergic population eliciting doses for multiple food allergens.
Abstract: To better understand the risk of exposure to food allergens, food challenge studies are designed to slowly increase the dose of an allergen delivered to allergic individuals until an objective reaction occurs. These dose-to-failure studies are used to determine acceptable intake levels and are analyzed using parametric failure time models. Though these models can provide estimates of the survival curve and risk, their parametric form may misrepresent the survival function for doses of interest. Different models that describe the data similarly may produce different dose-to-failure estimates. Motivated by predictive inference, we developed a Bayesian approach to combine survival estimates based on posterior predictive stacking, where the weights are formed to maximize posterior predictive accuracy. The approach defines a model space that is much larger than traditional parametric failure time modeling approaches. In our case, we use the approach to include random effects accounting for frailty components. The methodology is investigated in simulation, and is used to estimate allergic population eliciting doses for multiple food allergens.
TL;DR: This paper develops a procedure for summarizing the longitudinal data for the flexible piecewise HLM on the lines of Cook et al. (2004), and focuses on quantifying the overall treatment efficacy using the area under the curve (AUC) of the individual flexible piece wise HLM models.
Abstract: A core task in analyzing randomized clinical trials based on longitudinal data is to find the best way to describe the change over time for each treatment arm. We review the implementation and estimation of a flexible piecewise Hierarchical Linear Model (HLM) to model change over time. The flexible piecewise HLM consists of two phases with differing rates of change. The breakpoints between these two phases, as well as the rates of change per phase are allowed to vary between treatment groups as well as individuals. While this approach may provide better model fit, how to quantify treatment differences over the longitudinal period is not clear. In this paper, we develop a procedure for summarizing the longitudinal data for the flexible piecewise HLM on the lines of Cook et al. (2004). We focus on quantifying the overall treatment efficacy using the area under the curve (AUC) of the individual flexible piecewise HLM models. Methods are illustrated through data from a placebo-controlled trial in the treatment of depression comparing psychotherapy and pharmacotherapy.
TL;DR: In this article, the authors show that the collinearity between fixed effects and random effects in a spatial generalized linear mixed model can adversely affect estimates of the fixed effects, and that restricted spatio-temporal information can affect the estimation of fixed effects.
Abstract: Spatial confounding, that is, collinearity between fixed effects and random effects in a spatial generalized linear mixed model, can adversely affect estimates of the fixed effects. Restricted spat...