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  1. No Access

    Article

    Leveraging single-case results to Bayesian hierarchical modelling

    In scientific research, we often aim to learn one or more parameters of instances(objects) from a population—such as the batting averages of a group of baseball players and characteristics of white dwarfs from...

    Shi**g Si, Jia-wen Gu, Maozai Tian in Computational Statistics (2024)

  2. No Access

    Article

    Estimation and variable selection for generalized functional partially varying coefficient hybrid models

    In this article, we propose a novel class of generalized functional partially varying coefficient hybrid models and variable selection procedure in which the explanatory variables include infinite dimensional ...

    Yanxia Liu, Zhihao Wang, Maozai Tian, Keming Yu in Statistical Papers (2024)

  3. No Access

    Article

    Bayesian Local Influence for Spatial Autoregressive Models with Heteroscedasticity

    This paper studies Bayesian local influence analysis for the spatial autoregressive models with heteroscedasticity (heteroscedastic SAR models). Two local diagnostic procedures using curvature-based and slope-...

    **aowen Dai, Libin **, Maozai Tian, Lei Shi in Statistical Papers (2019)

  4. No Access

    Article

    Quantile regression for linear models with autoregressive errors using EM algorithm

    In this paper, we consider the quantile linear regression models with autoregressive errors. By incorporating the expectation–maximization algorithm into the considered model, the iterative weighted least squa...

    Yuzhu Tian, Manlai Tang, Yanchao Zang, Maozai Tian in Computational Statistics (2018)

  5. No Access

    Article

    Joint modeling for mixed-effects quantile regression of longitudinal data with detection limits and covariates measured with error, with application to AIDS studies

    It is very common in AIDS studies that response variable (e.g., HIV viral load) may be subject to censoring due to detection limits while covariates (e.g., CD4 cell count) may be measured with error. Failure t...

    Yuzhu Tian, Manlai Tang, Maozai Tian in Computational Statistics (2018)

  6. No Access

    Article

    An effective method to reduce the computational complexity of composite quantile regression

    In this article, we aim to reduce the computational complexity of the recently proposed composite quantile regression (CQR). We propose a new regression method called infinitely composite quantile regression (ICQ...

    Yanke Wu, Maozai Tian in Computational Statistics (2017)

  7. No Access

    Article

    Bayesian joint quantile regression for mixed effects models with censoring and errors in covariates

    In this paper, we discuss Bayesian joint quantile regression of mixed effects models with censored responses and errors in covariates simultaneously using Markov Chain Monte Carlo method. Under the assumption ...

    Yuzhu Tian, Er’qian Li, Maozai Tian in Computational Statistics (2016)

  8. No Access

    Article

    Simultaneous variable selection and parametric estimation for quantile regression

    In this paper, variable selection techniques in the linear quantile regression model are mainly considered. Based on the penalized quantile regression model, a one-step procedure that can simultaneously perfor...

    Wei **ong, Maozai Tian in Journal of the Korean Statistical Society (2015)