Exposure to air pollutants causes a range of adverse health effects. These harmful effects
occur whenever and wherever people come into direct contact with air pollution. Therefore …

The expectation–maximization (EM) algorithm is a familiar tool for computing the maximum
likelihood estimate of the parameters in hidden Markov and semi‐Markov models. This …

This paper develops a quantile hidden semi-Markov regression to jointly estimate multiple
quantiles for the analysis of multivariate time series. The approach is based upon the …

Increasingly, behavioral scientists encounter data where several individuals were measured
on multiple variables over numerous occasions. Many current methods combine these data …

We propose a new set of indices to capture the multidimensionality of a country's institutional
setting. Our indices are obtained by employing a dimension reduction approach on the …

Abstract Hidden Markov models (HMMs) have been extensively used in the univariate and
multivariate literature. However, there has been an increased interest in the analysis of …

Latent variable hidden Markov models (LVHMMs) are important statistical methods in
exploring the possible heterogeneity of data and explaining the pattern of subjects moving …

A nonhomogeneous hidden semi-Markov model is proposed to segment bivariate time
series of wind and wave directions according to a finite number of latent regimes and …

We present a method for dimension reduction of multivariate longitudinal data, where new
variables are assumed to follow a latent Markov model. New variables are obtained as …

Each year, a significant portion of the 40 million individuals in the United States who seek
care in emergency departments (EDs) following traumatic experiences develop adverse …