A Bayesian approach to a logistic regression model with incomplete information

Biometrics

Volumn 64, Issue 2, 2008, Pages 424-430

  Choi, Taeryon ^a   Schervish, Mark J ^b   Schmitt, Ketra A ^c   Small, Mitchell J ^d  

^a UNIVERSITY OF MARYLAND   (United States)

^b CARNEGIE MELLON UNIVERSITY   (United States)

^c BATTELLE MEMORIAL INSTITUTE   (United States)

^d CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

Aggregate information; Dose response study; Logistic regression; MCMC; WinBUGS

Indexed keywords

BAYESIAN NETWORKS; INORGANIC COMPOUNDS; MARKOV PROCESSES; REGRESSION ANALYSIS;

AGGREGATE INFORMATION; BAYESIAN APPROACHES; BERNOULLI TRIALS; COVARIATES; DOSE-RESPONSE STUDY; INCOMPLETE INFORMATION; LOGISTIC REGRESSION MODELING; LOGISTICS REGRESSIONS; MCMC; WINBUGS;

AGGREGATES;

ALGORITHM; BAYESIAN ANALYSIS; DOSE-RESPONSE RELATIONSHIP; ESTIMATION METHOD; LOGISTICS; MARKOV CHAIN; MODELING; MONTE CARLO ANALYSIS; PERCHLORATE; REGRESSION ANALYSIS; TOXICITY;

ALGORITHM; ARTICLE; BAYES THEOREM; BIOMETRY; LOGISTIC REGRESSION ANALYSIS; MATHEMATICAL ANALYSIS; MONTE CARLO METHOD;

EID: 43749084812     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/j.1541-0420.2007.00887.x     Document Type: Article

References (10)