RT - Journal Article T1 - Bayesian and Iterative Maximum Likelihood Estimation of the Coefficients in Logistic Regression Analysis with Linked Data JF - srtc-jsri YR - 2012 JO - srtc-jsri VO - 9 IS - 1 UR - http://jsri.srtc.ac.ir/article-1-68-en.html SP - 43 EP - 60 K1 - Record linkage K1 - logistic regression K1 - mixture distributions K1 - EM algorithm AB - This paper considers logistic regression analysis with linked data. It is shown that, in logistic regression analysis with linked data, a finite mixture of Bernoulli distributions can be used for modeling the response variables. We proposed an iterative maximum likelihood estimator for the regression coefficients that takes the matching probabilities into account. Next, the Bayesian counterpart of the frequentist model is developed. Then, a simulation study is performed to check the applicability and performance of the proposed frequentist and Bayesian methodologies encountering mismatch. LA eng UL http://jsri.srtc.ac.ir/article-1-68-en.html M3 10.18869/acadpub.jsri.9.1.43 ER -