In this proposal, we propose Bayesian and frequentist methodology for local influence diagnostics and develop model assessment tools for complete data settings as well as in the presence of missing covariate and/or response data for a variety of statistical models, including generalized linear models, models for longitudinal data, and survival model.
In Specific Aim 1, we develop frequentist local influence measures and goodness of fit statistics based on the general local influence development of Cook (1986), and discuss these measures for i) linear models with missing at random (MAR) and nonignorably missing covariates and ii) generalized linear models with MAR and nonignorably missing covariates.
For Specific Aim 2, we develop new classes of Bayesian case influence diagnostics for the complete data setting then generalize these diagnostics to the missing data framework. The proposed methodologies in Aims 1-2 are primarily motivated from several studies in the PI's collaborative work.
We propose Bayesian and frequentist methodology for local and case influence diagnostics and develop model assessment tools for complete data settings as well as in the presence of missing covariate and/or response data for a variety of statistical models, including generalized linear models, models for longitudinal data, and survival models. The proposed methodology is very useful in applications involving chronic diseases, such as cancer and AIDS.
Ankerst, Donna P; Goros, Martin; Tomlins, Scott A et al. (2018) Incorporation of Urinary Prostate Cancer Antigen 3 and TMPRSS2:ERG into Prostate Cancer Prevention Trial Risk Calculator. Eur Urol Focus : |
Rao, Shangbang; Ibrahim, Joseph G; Cheng, Jian et al. (2016) SR-HARDI: Spatially Regularizing High Angular Resolution Diffusion Imaging. J Comput Graph Stat 25:1195-1211 |
Joeng, Hee-Koung; Chen, Ming-Hui; Kang, Sangwook (2016) Proportional exponentiated link transformed hazards (ELTH) models for discrete time survival data with application. Lifetime Data Anal 22:38-62 |
Fraser, Raphael André; Lipsitz, Stuart R; Sinha, Debajyoti et al. (2016) Approximate median regression for complex survey data with skewed response. Biometrics 72:1336-1347 |
Wang, Xia; Chen, Ming-Hui; Kuo, Rita C et al. (2015) BAYESIAN SPATIAL-TEMPORAL MODELING OF ECOLOGICAL ZERO-INFLATED COUNT DATA. Stat Sin 25:189-204 |
Zhu, Hongtu; Ibrahim, Joseph G; Chen, Ming-Hui (2015) Diagnostic Measures for the Cox Regression Model with Missing Covariates. Biometrika 102:907-923 |
Lachos, Victor H; Chen, Ming-Hui; Abanto-Valle, Carlos A et al. (2015) Quantile regression for censored mixed-effects models with applications to HIV studies. Stat Interface 8:203-215 |
Lipsitz, Stuart R; Fitzmaurice, Garrett M; Arriaga, Alex et al. (2015) Using the jackknife for estimation in log link Bernoulli regression models. Stat Med 34:444-53 |
M'lan, Cyr Emile; Chen, Ming-Hui (2015) Objective Bayesian Inference for Bilateral Data. Bayesian Anal 10:139-170 |
Guo, Ruixin; Ahn, Mihye; Zhu, Hongtu et al. (2015) Spatially Weighted Principal Component Analysis for Imaging Classification. J Comput Graph Stat 24:274-296 |
Showing the most recent 10 out of 112 publications