Hidden semi-Markov-switching quantile regression for time series

Non-homogeneous hidden Markov-switching models for wind time series

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Application of Hidden Markov Models and Hidden Semi-Markov Models to Financial Time Series

Semi-parametric Quantile Regression for Analysing Continuous Longitudinal Responses

Recently, quantile regression (QR) models are often applied for longitudinal data analysis. When the distribution of responses seems to be skew and asymmetric due to outliers and heavy-tails, QR models may work suitably. In this paper, a semi-parametric quantile regression model is developed for analysing continuous longitudinal responses. The error term's distribution is assumed to be Asymmetr...

Hidden semi-Markov models

Article history: Received 14 April 2009 Available online 17 November 2009

Hidden Markov Model Regression

Hidden Markov Model Regression (HMMR) is an extension of the Hidden Markov Model (HMM) to regression analysis. We assume that the parameters of the regression model are determined by the outcome of a nite-state Markov chain and that the error terms are conditionally independent normally distributed with mean zero and state dependent variance. The theory of HMM regression is quite new, but some ...