This dissertation applies the Bayesian approach as a method to improve the estimation efficiency of existing econometric tools. The first chapter suggests the Continuous Choice Bayesian (CCB) estimator which combines the Bayesian approach with the Continuous Choice (CC) estimator suggested by Imai and Keane (2004). Using simulation study, I provide two important findings. First, the CC estimato...

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...

An example was given in the textbook All of Statistics (Wasserman, 2004, pages 186-188) for arguing that, in the problems with a great many parameters Bayesian inferences are weak, because they rely heavily on the likelihood function that captures information of only a tiny fraction of the total parameters. Alternatively he suggested non-Bayesian Horwitz-Thompson estimator, which cannot be obta...

Following a Bayesian statistical inference paradigm, we provide an alternative methodology for analyzing a multivariate logistic regression. We use a multivariate normal prior in the Bayesian analysis. We present a unique Bayes estimator associated with a prior which is admissible. The Bayes estimators of the coefficients of the model are obtained via MCMC methods. The proposed procedure...

Bian and Dickey (1996) developed a robust Bayesian estimator for the vector of regression coefficients using a Cauchy-type g-prior. This estimator is an adaptive weighted average of the least squares estimator and the prior location, and is of great robustness with respect to flat-tailed sample distribution. In this paper, we introduce the robust Bayesian estimator to the estimation of the Capi...

Samaniego and Reneau presented a landmark study on the comparison of Bayesian and frequentist point estimators. Their findings indicate that Bayesian point estimators work well in more situations than were previously suspected. In particular, their comparison reveals how a Bayesian point estimator can improve upon a frequentist point estimator even in situations where sharp prior knowledge is n...

This paper devoted a Bayesian and non-Bayesian estimation of the stress-strength reliability, , when X and Y two independent Type I generalized logistic distribution with common scale parameter. The maximum likelihood estimator and Bayes estimator are proposed for the stress strength reliability based on lower record values. The Bayesian and non-Bayesian confidence intervals for the reliability...

A maximum likelihood Bayesian estimator that recovers the signal component of the wavelet coefficients from original images by using an a-stable signal prior distribution is discussed. As we discussed in our earlier paper that the Bayesian estimator can approximate impulsive noise more accurately than other models and that the general case of the Bayesian processor does not have a closed-form e...