Bayesian adaptive lasso quantile regression with non-ignorable missing responses
Chen, Ranran Dao, Mai Ye, Keying Wang, Min
Chen, Ranran
Dao, Mai
Ye, Keying
Wang, Min
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Authors
Chen, Ranran
Dao, Mai
Ye, Keying
Wang, Min
Dao, Mai
Ye, Keying
Wang, Min
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2024-09-16
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Article
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Keywords
Bayesian adaptive lasso,High-dimensional analysis,Non-ignorable missing data,Quantile regression
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Citation
Chen, R., Dao, M., Ye, K. et al. Bayesian adaptive lasso quantile regression with non-ignorable missing responses. Comput Stat (2024).
Abstract
In this paper, we develop a fully Bayesian adaptive lasso quantile regression model to analyze data with non-ignorable missing responses, which frequently occur in various fields of study. Specifically, we employ a logistic regression model to deal with missing data of non-ignorable mechanism. By using the asymmetric Laplace working likelihood for the data and specifying Laplace priors for the regression coefficients, our proposed method extends the Bayesian lasso framework by imposing specific penalization parameters on each regression coefficient, enhancing our estimation and variable selection capability. Furthermore, we embrace the normal-exponential mixture representation of the asymmetric Laplace distribution and the Student-t approximation of the logistic regression model to develop a simple and efficient Gibbs sampling algorithm for generating posterior samples and making statistical inferences. The finite-sample performance of the proposed algorithm is investigated through various simulation studies and a real-data example. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.
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Springer Science and Business Media Deutschland GmbH
Journal
Computational Statistics
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0943-4062