Quantile regression for longitudinal data based on latent Markov subject-specific parameters
We propose a latent Markov quantile regression model for longitudinal data with non-informative drop-out. The observations, conditionally on covariates, are modeled through an asymmetric Laplace distribution. Random effects are assumed to be time-varying and to follow a first order latent Markov cha...
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| Published in | Statistics and computing Vol. 22; no. 1; pp. 141 - 152 |
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| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
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Springer US
01.01.2012
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| Abstract | We propose a latent Markov quantile regression model for longitudinal data with non-informative drop-out. The observations, conditionally on covariates, are modeled through an asymmetric Laplace distribution. Random effects are assumed to be time-varying and to follow a first order latent Markov chain. This latter assumption is easily interpretable and allows exact inference through an ad hoc EM-type algorithm based on appropriate recursions. Finally, we illustrate the model on a benchmark data set. |
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| AbstractList | We propose a latent Markov quantile regression model for longitudinal data with non-informative drop-out. The observations, conditionally on covariates, are modeled through an asymmetric Laplace distribution. Random effects are assumed to be time-varying and to follow a first order latent Markov chain. This latter assumption is easily interpretable and allows exact inference through an ad hoc EM-type algorithm based on appropriate recursions. Finally, we illustrate the model on a benchmark data set. |
| Author | Farcomeni, Alessio |
| Author_xml | – sequence: 1 givenname: Alessio surname: Farcomeni fullname: Farcomeni, Alessio email: Alessio.Farcomeni@uniroma1.it organization: Sapienza-University of Rome |
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| Keywords | Hidden Markov model Longitudinal data Quantile regression Asymmetric Laplace distribution |
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| Title | Quantile regression for longitudinal data based on latent Markov subject-specific parameters |
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