Browsing by Author "Martin, Ernesto San"

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    Linear mixed models with skew-elliptical distributions: A Bayesian approach
    (ELSEVIER SCIENCE BV, 2008) Jara, Alejandro; Quintana, Fernando; Martin, Ernesto San
    Normality of random effects and error terms is a routine assumption for linear mixed models. However, such an assumption may be unrealistic, obscuring important features of within- and among-unit variation. A simple and robust Bayesian parametric approach that relaxes this assumption by using a multivariate skew-elliptical distribution, which includes the Skew-t, Skew-normal, t-Student, and Normal distributions as special cases and provides flexibility in capturing a broad range of non-normal and asymmetric behavior is presented. An appropriate posterior simulation scheme is developed and the methods are illustrated with an application to a longitudinal data example. (C) 2008 Elsevier B.V. All rights reserved.
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    On the impact of missing outcomes in linear regression
    (2023) Alarcon-Bustamante, Eduardo; Varas, Ines M.; Martin, Ernesto San
    The linear regression model is commonly used for measuring the impact of covariates over an outcome of interest, which is typically measured through the regression coefficients of the model. However, the presence of missing outcomes can seriously affect this interpretation because we have no idea about the potential impact of the covariates on those units with missing outcomes. Here, we illustrate the consequences of the missing outcomes as the interpretation of the regression coefficients in the impact of the selection factors on the performance in the university.
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    On the relationships between sum score based estimation and joint maximum likelihood estimation
    (2008) Del Pino, Guido; Martin, Ernesto San; Gonzalez, Jorge; De Boeck, Paul
    This paper analyzes the sum score based (SSB) formulation of the Rasch model, where items and sum scores of persons are considered as factors in a logit model. After reviewing the evolution leading to the equality between their maximum likelihood estimates, the SSB model is then discussed from the point of view of pseudo-likelihood and of misspecified models. This is then employed to provide new insights into the origin of the known inconsistency of the difficulty parameter estimates in the Rasch model. The main results consist of exact relationships between the estimated standard errors for both models; and, for the ability parameters, an upper bound for the estimated standard errors of the Rasch model in terms of those for the SSB model, which are more easily available.