Random effects model

About: Random effects model is a research topic. Over the lifetime, 8388 publications have been published within this topic receiving 438823 citations. The topic is also known as: random effects & random effect.

Inverse Gaussian Processes With Random Effects and Explanatory Variables for Degradation Data

Abstract: Degradation models are widely used to assess the lifetime information of highly reliable products. This study proposes a degradation model based on an inverse normal-gamma mixture of an inverse Gaussian process. This article presents the properties of the lifetime distribution and parameter estimation using the EM-type algorithm, in addition to providing a simple model-checking procedure to assess the validity of different stochastic processes. Several case applications are performed to demonstrate the advantages of the proposed model with random effects and explanatory variables. Technical details, data, and R code are available online as supplementary materials.

A lagrange multiplier test for the error components model with incomplete panels

Abstract: This Paper extends the Breusch and Pagan(1980) lagrange Multiplier test for the random effects model to the incomplete panel data case. It is shown that this test retains the simple additive structure observed in the complete panel data case. It should prove useful for practitioners facing incomplete panel data applications.

Type I and Type II Error Under Random-Effects Misspecification in Generalized Linear Mixed Models

Abstract: Generalized linear mixed models (GLMMs) have become a frequently used tool for the analysis of non-Gaussian longitudinal data. Estimation is based on maximum likelihood theory, which assumes that the underlying probability model is correctly specified. Recent research is showing that the results obtained from these models are not always robust against departures from the assumptions on which these models are based. In the present work we have used simulations with a logistic random-intercept model to study the impact of misspecifying the random-effects distribution on the type I and II errors of the tests for the mean structure in GLMMs. We found that the misspecification can either increase or decrease the power of the tests, depending on the shape of the underlying random-effects distribution, and it can considerably inflate the type I error rate. Additionally, we have found a theoretical result which states that whenever a subset of fixed-effects parameters, not included in the random-effects structure equals zero, the corresponding maximum likelihood estimator will consistently estimate zero. This implies that under certain conditions a significant effect could be considered as a reliable result, even if the random-effects distribution is misspecified.

Alternative methods for the quantitative analysis of panel data in family research : pooled time-series models

Abstract: This article examines quantitative panel analysis techniques appropriate for situations in which the researcher models determinants of change in continuous outcomes. One set of techniques, based on the analysis of pooled time-series data sets, has received little attention in the family literature, although there are a number of situations where these techniques would be appropriate, such as the analysis of multiple-wave panel data. The article explores the fixed effect (change-scare) and random effects models based on pooled time-series data and discusses their advantages and disadvantages for the analysis of survey panel data compared with other available approaches. An empirical example of these methods is presented in which the effect of marital duration and number of children on spousal interaction in intact marriages was examined in a four-wave panel sample. A significant development in the field of family research has been the increased availability of large panel survey data sets containing variables of interest to family researchers (e.g., National Survey of Families and Households Panel, Panel Study of Income Dynamics, National Longitudinal Survey of Youth, Marital Instability Over the Life Course Panel). The potential posed by these panel studies is often unrealized because of the greater complexity of panel analyses and the shortage of clear and accessible guidelines for selecting the appropriate analysis models and statistical software. Several guides to panel methods are available (e.g., Campbell, Mutran, & Parker, 1986; Collins & Horn, 1991; Finkel, 1995; Johnson, 1988; Kessler & Greenberg, 1981; Markus, 1979; Menard, 1991). While these guides can be of value, they have the following drawbacks: Some recent developments are not covered, the focus has been largely on methods for two-wave panels, and situations commonly encountered by family researchers in the analysis of survey data, such as missing waves for some respondents, have been neglected. One type of model commonly found in family research involves the analysis of change over time in a continuous dependent variable. For example, a researcher interested in explaining change in marital interaction over the course of a marriage may model the effects of increased duration of the marriage, changes in the number and ages of the children, spells of employment of both spouses, and changes in family income. This situation involves a continuous dependent variable (degree of spousal interaction) with continuous variables (marital duration, income) and events (addition and subtraction of children, spells of employment and unemployment) as explanatory variables. Panel studies of changes in psychological distress brought about by marital dissolution, changes in frequency of sexual intercourse over the duration of the marriage, and the effect of retirement on marital happiness are research problems with similar analytic needs. With two or more waves of panel data and a continuous dependent variable, the researcher has a choice among five basic panel analysis models. These are (a) regression with lagged dependent variables, (b) structural equation models with reciprocal and lagged effects (e.g., LISREL), (c) repeated measures analysis of variance, (d) growth curve and hierarchical effects models, and (e) fixed (change-score) and random effects regression estimators for pooled time-series data sets. Event history, or hazard, models (Teachman, 1982) and panel models for qualitative variables (Clogg, Eliason, & Grego, 1990) are excluded from this list because they are not designed for use with continuous dependent variables. The first three techniques have been widely used with panel data in the family literature, and are generally accessible to the researcher. Many of the regression techniques have been superseded by the structural equation approaches, which are capable of estimating reciprocal effects and control for biases introduced by measurement errors and autocorrelated errors. …

Hierarchical Generalized Linear Models and Frailty Models with Bayesian Nonparametric Mixing

Abstract: SUMMARY This paper proposes Bayesian nonparametric mixing for some well-known and popular models. The distribution of the observations is assumed to contain an unknown mixed effects term which includes a fixed effects term, a function of the observed covariates, and an additive or multiplicative random effects term. Typically these random effects are assumed to be independent of the observed covariates and independent and identically distributed from a distribution from some known parametric family. This assumption may be suspect if either there is interaction between observed covariates and unobserved covariates or the fixed effects predictor of observed covariates is misspecified. Another cause for concern might be simply that the covariates affect more than just the location of the mixed effects distribution. As a consequence the distribution of the random effects could be highly irregular in modality and skewness leaving parametric families unable to model the distribution adequately. This paper therefore proposes a Bayesian nonparametric prior for the random effects to capture possible deviances in modality and skewness and to explore the observed covariates' effect on the distribution of the mixed effects.