Journal ArticleDOI
Skew-normal Linear Mixed Models
TLDR
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.read more
Citations
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Journal ArticleDOI
The Skew-normal Distribution and Related Multivariate Families.
TL;DR: In this paper, the authors provide an introductory overview of a portion of distribution theory which is currently under intense development and illustrate connections with various areas of application, including selective sampling, models for compositional data, robust methods, some problems in econometrics, non-linear time series, especially in connection with financial data, and more.
Book
The Skew-Normal and Related Families
TL;DR: This comprehensive treatment, blending theory and practice, will be the standard resource for statisticians and applied researchers, and Assuming only basic knowledge of (non-measure-theoretic) probability and statistical inference, the book is accessible to the wide range of researchers who use statistical modelling techniques.
Journal ArticleDOI
Closed-skew normality in stochastic frontiers with individual effects and long/short-run efficiency
TL;DR: In this paper, the authors considered the estimation of Kumbhakar et al. (KLH) four random components stochastic frontier (SF) model using MLE techniques and derived the log-likelihood function of the model using results from the closed-skew normal distribution.
Proceedings Article
Likelihood based inference for skew-normal independent linear mixed models
TL;DR: In this article, a new class of asym- metric linear mixed models that provides for an efficient estimation of the parame- ters in the analysis of longitudinal data is presented. But the accuracy of the assumed normal distribu- tion is crucial for valid inference of the parameters.
Journal ArticleDOI
FIRM HETEROGENEITY, PERSISTENT AND TRANSIENT TECHNICAL INEFFICIENCY: A GENERALIZED TRUE RANDOM-EFFECTS model
TL;DR: In this article, the authors consider a panel data stochastic frontier model that disentangles unobserved firm effects (firm heterogeneity) from persistent and time-varying technical inefficiency.
References
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Journal ArticleDOI
Maximum likelihood from incomplete data via the EM algorithm
Book
Time series analysis, forecasting and control
TL;DR: In this article, a complete revision of a classic, seminal, and authoritative book that has been the model for most books on the topic written since 1970 is presented, focusing on practical techniques throughout, rather than a rigorous mathematical treatment of the subject.
Journal ArticleDOI
Time Series Analysis Forecasting and Control
TL;DR: This revision of a classic, seminal, and authoritative book explores the building of stochastic models for time series and their use in important areas of application forecasting, model specification, estimation, and checking, transfer function modeling of dynamic relationships, modeling the effects of intervention events, and process control.
Book
Mixed-Effects Models in S and S-PLUS
TL;DR: Linear Mixed-Effects and Nonlinear Mixed-effects (NLME) models have been studied in the literature as mentioned in this paper, where the structure of grouped data has been used for fitting LME models.
Journal ArticleDOI
Random-effects models for longitudinal data
Nan M. Laird,James H. Ware +1 more
TL;DR: In this article, a unified approach to fitting two-stage random-effects models, based on a combination of empirical Bayes and maximum likelihood estimation of model parameters and using the EM algorithm, is discussed.