|Grant Number:||5R01CA074015-12 Interpret this number|
|Primary Investigator:||Ibrahim, Joseph|
|Organization:||Univ Of North Carolina Chapel Hill|
|Project Title:||Inference in Regression Models with Missing Covariates|
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.
Bayesian dynamic regression models for interval censored survival data with application to children dental health.
Authors: Wang X. , Chen M.H. , Yan J. .
Source: Lifetime data analysis, 2013 Jul; 19(3), p. 297-316.
EPub date: 2013-02-07.
ZINBA integrates local covariates with DNA-seq data to identify broad and narrow regions of enrichment, even within amplified genomic regions.
Authors: Rashid N.U. , Giresi P.G. , Ibrahim J.G. , Sun W. , Lieb J.D. .
Source: Genome biology, 2011; 12(7), p. R67.
EPub date: 2011-07-25.
Choosing among partition models in Bayesian phylogenetics.
Authors: Fan Y. , Wu R. , Chen M.H. , Kuo L. , Lewis P.O. .
Source: Molecular biology and evolution, 2011 Jan; 28(1), p. 523-32.
EPub date: 2010-08-27.
Local influence for generalized linear models with missing covariates.
Authors: Shi X. , Zhu H. , Ibrahim J.G. .
Source: Biometrics, 2009 Dec; 65(4), p. 1164-74.
A note on the validity of statistical bootstrapping for estimating the uncertainty of tensor parameters in diffusion tensor images.
Authors: Yuan Y. , Zhu H. , Ibrahim J.G. , Lin W. , Peterson B.S. .
Source: IEEE transactions on medical imaging, 2008 Oct; 27(10), p. 1506-14.
Bayesian error-in-variable survival model for the analysis of GeneChip arrays.
Authors: Tadesse M.G. , Ibrahim J.G. , Gentleman R. , Chiaretti S. , Ritz J. , Foa R. .
Source: Biometrics, 2005 Jun; 61(2), p. 488-97.
Wavelet thresholding with bayesian false discovery rate control.
Authors: Tadesse M.G. , Ibrahim J.G. , Vannucci M. , Gentleman R. .
Source: Biometrics, 2005 Mar; 61(1), p. 25-35.
Identification of differentially expressed genes in high-density oligonucleotide arrays accounting for the quantification limits of the technology.
Authors: Tadesse M.G. , Ibrahim J.G. , Mutter G.L. .
Source: Biometrics, 2003 Sep; 59(3), p. 542-54.
Non-ignorable missing covariates in generalized linear models.
Authors: Lipsitz S.R. , Ibrahim J.G. , Chen M.H. , Peterson H. .
Source: Statistics in medicine, 1999 Sep 15-30; 18(17-18), p. 2435-48.
Using missing data methods in genetic studies with missing mutation status.
Authors: Leong T. , Lipsitz S.R. , Ibrahim J.G. .
Source: Statistics in medicine, 1999-02-28; 18(4), p. 473-85.