Efficient direct sampling MCEM algorithm for latent variable models with binary responses

Semiparametric change points detection using single index spatial random effects model in environmental epidemiology study

Environmental health studies are of great interest in research to evaluate the mortality-temperature relationship by adjusting spatially correlated random effects as well as identifying significant change points in temperature. However, this relationship is often not expressed using parametric models, which makes identifying change points an even more challenging problem. This paper proposes a unified semiparametric approach to simultaneously identify the nonlinear mortality-temperature relationship and detect spatially-dependent change points. A unified method is proposed for the model estimation, spatially dependent change points detection, and testing whether they are significant simultaneously by a permutation-based test. We operate under the assumption that change points remain constant, yet acknowledge the uncertainty regarding their precise number. These change points are influenced by the smoothing of an unknown function, which in turn relies on a smoothing variable and spatial random effects. Consequently, the detection of change points may be influenced by spatial effects. In this paper, several simulation studies are conducted to evaluate the performance of our proposed approach. The advantages of this unified approach are demonstrated using epidemiological data on mortality and temperature.

On Components, Latent Variables, PLS and Simple Methods: Reactions to Rigdon's Rethinking of PLS

Rigdon (2012) suggests that partial least squares (PLS) can be improved by killing it, that is, by making it into a different methodology based on components. We provide some history on problems with component-type methods and develop some implications of Rigdon's suggestion. It seems more appropriate to maintain and improve PLS as far as possible, but also to freely utilize alternative models and methods when those are more relevant in certain data analytic situations. Huang's (2013) new consistent and efficient PLSe2 methodology is suggested as a candidate for an improved PLS.

Limited Information Estimation in Binary Factor Analysis: A Review and Extension

Based on the Bayes modal estimate of factor scores in binary latent variable models, this paper proposes two new limited information estimators for the factor analysis model with a logistic link function for binary data based on Bernoulli distributions up to the second and the third order with maximum likelihood estimation and Laplace approximations to required integrals. These estimators and two existing limited information weighted least squares estimators are studied empirically. The limited information estimators compare favorably to full information estimators based on marginal maximum likelihood, MCMC, and multinomial distribution with a Laplace approximation methodology. Among the various estimators, Maydeu-Olivares and Joe's (2005) weighted least squares limited information estimators implemented with Laplace approximations for probabilities are shown in a simulation to have the best root mean square errors.