Adapting Predictive Models for Cepheid Variable Star Classification Using Linear Regression and Maximum Likelihood

A Machine Learning Approach to Cepheid Variable Star Classification Using Data Alignment and Maximum Likelihood

Our study centers on the classification of two subtypes of Cepheid variable stars. Such classification is relatively easy to obtain for nearby galaxies, but as we incorporate new galaxies, the cost of labeling stars calls for some form of model adaptation. Adapting a predictive model to differentiate Cepheids across galaxies is difficult because of the sample bias problem in star distribution (...

Speaker Adaptation using Maximum Likelihood Linear Regression

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...

Fast speaker adaptation using eigenspace-based maximum likelihood linear regression

This paper presents an eigenspace-based fast speaker adaptation approach which can improve the modeling accuracy of the conventional maximum likelihood linear regression (MLLR) techniques when only very limited adaptation data is available. The proposed eigenspace-based MLLR approach was developed by introducing a priori knowledge analysis on the training speakers via PCA, so as to construct an...

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.