Linear Maximum Likelihood Regression Analysis for Untransformed Log-Normally Distributed Data

Bayesian and Iterative Maximum Likelihood Estimation of the Coefficients in Logistic Regression Analysis with Linked Data

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...

Maximum Likelihood Estimation in Log-linear

We study maximum likelihood estimation in log-linear models under conditional Poisson sampling schemes. We derive necessary and sufficient conditions for existence of the maximum likelihood estimator (MLE) of the model parameters and investigate estimability of the natural and mean-value parameters under a nonexistent MLE. Our conditions focus on the role of sampling zeros in the observed table...

Maximum Likelihood Estimation in Log - Linear Models

We study maximum likelihood estimation in log-linear models under conditional Poisson sampling schemes. We derive necessary and sufficient conditions for existence of the maximum likelihood estimator (MLE) of the model parameters and investigate estimability of the natural and mean-value parameters under a non-existent MLE. Our conditions focus on the role of sampling zeros in the observed tabl...

Approximate Maximum Likelihood Estimation in Linear Regression*

A b s t r a c t. The application of the ML method in linear regression requires a parametric form for the error density. When this is not available, the density may be parameterized by its cumulants (~i) and the ML then applied. Results , (i+2)/2 are obtained when the standardized cumulants (~/~) satisfy ~/~ = ~i+2/~2 = O(v i) as v-~ 0 for i > 0.

Speaker Adaptation using Maximum Likelihood Linear Regression