TY - JOUR
DP - National Agricultural Library
DB - PubAg
JO - Biometrics
TI - A Bayesian Approach to a Logistic Regression Model with Incomplete Information
A1 - Choi, Taeryon
A4 - Choi, Taeryon
A4 - Schervish, Mark J.
A4 - Schmitt, Ketra A.
A4 - Small, Mitchell J.
EP - 2008 v.64 no.2
KW - Markov chain
KW - algorithms
KW - biometry
KW - dose response
KW - regression analysis
KW - toxic substances
AN - 218657
AB - We consider a set of independent Bernoulli trials with possibly different success probabilities that depend on covariate values. However, the available data consist only of aggregate numbers of successes among subsets of the trials along with all of the covariate values. We still wish to estimate the parameters of a modeled relationship between the covariates and the success probabilities, e.g., a logistic regression model. In this article, estimation of the parameters is made from a Bayesian perspective by using a Markov chain Monte Carlo algorithm based only on the available data. The proposed methodology is applied to both simulation studies and real data from a doseâresponse study of a toxic chemical, perchlorate.
PY - 2008
LA -
DA - 2008-06
VL - v. 64
IS - no. 2
SP - pp. 424-430
DO - 10.1111/j.1541-0420.2007.00887.x
ER -