The technique of regression, in particular linear regression, is the
most popular statistical tool. There are several forms of regression
such as linear, non-linear, simple, multiple, parametric and
non-parametric.The major purpose of regression is to explore the
dependence of one variable on other. One is called as response
variable and other is known to be predictor variable. The goal is to
forecast the values of the dependent variables on the basis of one or,
more explanatory variables by setting up an appropriate probability
model. The type of model depends on the types of variables involved.
The talk will contain the different models to represent linear
regression such as mathematical model, statistical model and the
probabilistic model. The emphasis will be given to least square
criterion of regression. I will also discuss some fundamental aspects
of "maximum likelihood linear regression (MLLR)" as a transformation
approach to speaker adaptation. MLLR is a method that transforms
mixture components that are transformed similarly and introduces the
concept of regression classes as a set of mixture components that are
transformed similarly.
Additional items of interest: