Victor H. Lachos

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A Bayesian analysis of stochastic volatility (SV) models using the class of symmetric scale mixtures of normal (SMN) distributions is considered. In the face of non-normality, this provides an appealing robust alternative to the routine use of the normal distribution. Specific distributions examined include the normal, student-t, slash and the variance(More)
Linear mixed model with normally distributed response are routinely used in longitudinal data. However, the accuracy of the assumed normal distribution is crucial for valid inference of the parameters. In this article we present a new class of asymmetric linear mixed models that provides an efficient estimation of the parameters in the analysis of(More)
HIV RNA viral load measures are often subjected to some upper and lower detection limits depending on the quantification assays. Hence, the responses are either left or right censored. Linear (and nonlinear) mixed-effects models (with modifications to accommodate censoring) are routinely used to analyze this type of data and are based on normality(More)
Linear mixed models were developed to handle clustered data and have been a topic of increasing interest in statistics for the past fifty years. Generally, the normality (or symmetry) of the random effects is a common assumption in linear mixed models but it may, sometimes, be unrealistic, obscuring important features of among-subjects variation. In this(More)
Continuous (clustered) proportion data often arise in various domains of medicine and public health where the response variable of interest is a proportion (or percentage) quantifying disease status for the cluster units, ranging between zero and one. However, because of the presence of relatively disease-free as well as heavily diseased subjects in any(More)
An extension of some standard likelihood based procedures to heteroscedastic nonlinear regression models under scale mixtures of skew-normal (SMSN) distributions is developed. We derive a simple EM-type algorithm for iteratively computing maximum likelihood (ML) estimates and the observed information matrix is derived analytically. Simulation studies(More)
The zero-in ated negative binomial model is used to account for overdispersion detected in data that are initially analyzed under the zero-in ated Poisson model. We consider a frequentist analysis, a jackknife estimator and non-parametric bootstrap for parameter estimation of zero-in ated negative binomial regression models. In addition, an EM-type(More)