Parallel maximum likelihood estimator for multiple linear regression models

Maximum Weighted Likelihood Estimator in Logistic Regression

The least weighted squares estimator is a well known technique in robust regression. Its likelihood analogy in logistic regression is the maximum weighted likelihood estimator, proposed in Vandev and Neykov (1998) and Mueller and Neykov (2003). This article mentions already proved properties, shows its inconsistency and compare it to the other estimators by an extensive simulation. Introduction...

Corrected Maximum Likelihood Estimators in Linear Heteroskedastic Regression Models*

The linear heteroskedastic regression model, for which the variance of the response is given by a suitable function of a set of linear exogenous variables, is very useful in econometric applications. We derive a simple matrix formula for the n biases of the maximum likelihood estimators of the parameters in the variance of the response, where n is the sample size. These biases are easily obtain...

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

Maximum Likelihood Estimator for Hidden Markov Models in Continuous Time

The paper studies large sample asymptotic properties of the Maximum Likelihood Estimator (MLE) for the parameter of a continuous time Markov chain, observed in white noise. Using the method of weak convergence of likelihoods due to I.Ibragimov and R.Khasminskii [14], consistency, asymptotic normality and convergence of moments are established for MLE under certain strong ergodicity conditions o...