Variational Bayesian Inference for Quantile Regression Models with Nonignorable Missing Data

Variational Bayesian Inference for Quantile Regression Models with Nonignorable Missing Data

Quantile regression models are remarkable structures for conducting regression analyses when the data are subject to missingness. Missing values occur because of various factors like missing completely at random, missing at random, or missing not at random. All these may result from system malfuncti...

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Bibliographic Details
Main Authors: Xiaoning Li, Mulati Tuerde, Xijian Hu
Format: Article
Language:English
Published: MDPI AG 2023-09-01
Series:Mathematics
Subjects:
lasso regularization
nonignorable missing data
quantile regression
variational Bayesian inference
Online Access:https://www.mdpi.com/2227-7390/11/18/3926

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