Bayesian Estimation of Partial Functional Tobit Censored Quantile Regression Model
Fuente:
Wiley Open Access
Enregistré dans:
| Auteurs principaux: | , , , |
|---|---|
| Format: | Artículo Open Access |
| Publié: |
Wiley
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1867009952018595840 |
|---|---|
| author | Chunjie Wang Zhexin Lu Chuchu Wang Xinyuan Song |
| author_facet | Chunjie Wang Zhexin Lu Chuchu Wang Xinyuan Song Chunjie Wang Zhexin Lu Chuchu Wang Xinyuan Song |
| collection | Wiley Open Access |
| contents | Bayesian Estimation of Partial Functional Tobit Censored Quantile Regression Model Chunjie Wang Zhexin Lu Chuchu Wang Xinyuan Song Statistics in Medicine ABSTRACTThe information extracted from imaging data has become increasingly important in disease diagnosis as it uncovers associations between imaging features and diseases of interest. This study proposes a partial functional Tobit censored quantile regression (PFTCQR) model to investigate the quantile‐specific relationships between the time of incidence of laryngeal cancer and a set of imaging and clinical predictors based on the data collected from a laryngeal cancer study in the Otolaryngology Department of a tertiary hospital in Jilin Province, China. The functional principal component analysis and moment method are employed to estimate the slope and covariance functions of the functional predictors. An efficient Markov chain Monte Carlo (MCMC) algorithm is developed, leveraging the location‐scale mixture representation of the asymmetric Laplace distribution (ALD) to perform the estimation. Furthermore, we extend the PFTCQR model to the composite quantile regression framework and incorporate variable selection for scalar covariates, further enhancing the robustness and efficiency of parameter estimation and improving model fitting. The proposed method is demonstrated through simulation studies and applied to the laryngeal carcinoma data. Results provide new insights into potential risk factors for laryngeal carcinoma and their effects varying across quantiles. Specific laryngeal regions are identified as significantly associated with the progression of the disease. 10.1002/sim.70152 http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| doi_str_mv | 10.1002/sim.70152 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_sim_70152 |
| institution | Wiley Open Access |
| license_str_mv | http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| publishDate | 2025 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | Bayesian Estimation of Partial Functional Tobit Censored Quantile Regression Model Chunjie Wang Zhexin Lu Chuchu Wang Xinyuan Song Statistics in Medicine Bayesian Estimation of Partial Functional Tobit Censored Quantile Regression Model Chunjie Wang Zhexin Lu Chuchu Wang Xinyuan Song Statistics in Medicine ABSTRACTThe information extracted from imaging data has become increasingly important in disease diagnosis as it uncovers associations between imaging features and diseases of interest. This study proposes a partial functional Tobit censored quantile regression (PFTCQR) model to investigate the quantile‐specific relationships between the time of incidence of laryngeal cancer and a set of imaging and clinical predictors based on the data collected from a laryngeal cancer study in the Otolaryngology Department of a tertiary hospital in Jilin Province, China. The functional principal component analysis and moment method are employed to estimate the slope and covariance functions of the functional predictors. An efficient Markov chain Monte Carlo (MCMC) algorithm is developed, leveraging the location‐scale mixture representation of the asymmetric Laplace distribution (ALD) to perform the estimation. Furthermore, we extend the PFTCQR model to the composite quantile regression framework and incorporate variable selection for scalar covariates, further enhancing the robustness and efficiency of parameter estimation and improving model fitting. The proposed method is demonstrated through simulation studies and applied to the laryngeal carcinoma data. Results provide new insights into potential risk factors for laryngeal carcinoma and their effects varying across quantiles. Specific laryngeal regions are identified as significantly associated with the progression of the disease. 10.1002/sim.70152 http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| title | Bayesian Estimation of Partial Functional Tobit Censored Quantile Regression Model |
| topic | Statistics in Medicine |
| url | https://onlinelibrary.wiley.com/doi/10.1002/sim.70152 |