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Communications in Statistics - Simulation and Computation Volume 40, 2011 - Issue 8
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Original Articles

Bayesian Proportional Odds Models for Analyzing Current Status Data: Univariate, Clustered, and Multivariate

Xiaoyan Lin Department of Statistics, University of South Carolina, Columbia, South Carolina, USA
&
Lianming Wang Department of Statistics, University of South Carolina, Columbia, South Carolina, USACorrespondence[email protected]
Pages 1171-1181 | Received 17 Jun 2010, Accepted 15 Feb 2011, Published online: 19 Apr 2011

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Read on this site (5)

Yanqin Feng, Jie Wang & Yang Li. (2023) Goodness-of-fit inference for the additive hazards regression model with clustered current status data. Journal of Applied Statistics 50:9, pages 1921-1941.
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Lu Wang & Lianming Wang. (2022) An EM algorithm for analyzing right-censored survival data under the semiparametric proportional odds model. Communications in Statistics - Theory and Methods 51:15, pages 5284-5297.
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Chun Pan, Bo Cai, Lianming Wang & Xiaoyan Lin. (2022) ICBayes: a package of Bayesian semiparametric regression for intervel-censored data. Communications in Statistics - Simulation and Computation 51:4, pages 1852-1866.
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Haiming Zhou & Timothy Hanson. (2018) A Unified Framework for Fitting Bayesian Semiparametric Models to Arbitrarily Censored Survival Data, Including Spatially Referenced Data. Journal of the American Statistical Association 113:522, pages 571-581.
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Pavithra Hariharan, P. G. Sankaran & Asokan Mulayath Variyath. A Bayesian semiparametric regression model for current status data. Communications in Statistics - Simulation and Computation 0:0, pages 1-13.
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Articles from other publishers (12)

Pavithra Hariharan & P. G. Sankaran. (2024) Semiparametric regression modelling of current status competing risks data: a Bayesian approach. Computational Statistics 39:4, pages 2083-2108.
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Xifen Huang, Chaosong Xiong, Tao Jiang, Junfeng Lu & Jinfeng Xu. (2022) Efficient Estimation and Inference in the Proportional Odds Model for Survival Data. Mathematics 10:18, pages 3362.
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Olga Alexandra Bustos Giraldo, Mario César Jaramillo Elorza & Carlos Mario Lopera Gómez. (2022) Comparación de algunos métodos para estimar el modelo de riesgos proporcionales de Cox para datos con censura a intervalo. Ciencia en Desarrollo 13:1, pages 79-92.
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Lu Wang & Lianming Wang. (2021) Regression analysis of arbitrarily censored survival data under the proportional odds model. Statistics in Medicine 40:16, pages 3724-3739.
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Alhassan Faisal. (2021) PENALIZED LOG-LIKELIHOOD ESTIMATION FOR COX-FRAILTY MODEL WITH NONINFORMATIVE BIVARIATE CURRENT STATUS DATA. INDIAN JOURNAL OF APPLIED RESEARCH, pages 16-27.
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Chun Pan, Bo Cai & Lianming Wang. (2020) A Bayesian approach for analyzing partly interval-censored data under the proportional hazards model. Statistical Methods in Medical Research 29:11, pages 3192-3204.
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Prabhashi W. Withana Gamage, Christopher S. McMahan, Lianming Wang & Wanzhu Tu. (2018) A Gamma-frailty proportional hazards model for bivariate interval-censored data. Computational Statistics & Data Analysis 128, pages 354-366.
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Xiaoyan Lin, Bo Cai, Lianming Wang & Zhigang Zhang. (2014) A Bayesian proportional hazards model for general interval-censored data. Lifetime Data Analysis 21:3, pages 470-490.
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Li Li, Timothy Hanson & Jiajia Zhang. (2015) Spatial Extended Hazard Model with Application to Prostate Cancer Survival. Biometrics 71:2, pages 313-322.
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Naichen Wang, Lianming Wang & Christopher S. McMahan. (2015) Regression analysis of bivariate current status data under the Gamma-frailty proportional hazards model using the EM algorithm. Computational Statistics & Data Analysis 83, pages 140-150.
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Haiming Zhou & Timothy Hanson. 2015. Nonparametric Bayesian Inference in Biostatistics. Nonparametric Bayesian Inference in Biostatistics 215 246 .
Fuchang Gao & Jon A. Wellner. (2013) Global rates of convergence of the MLE for multivariate interval censoring. Electronic Journal of Statistics 7:none.
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