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where Y is an n×1 column vector of random variables, X is an n×p matrix of "known" (i.e. observable and non-random) quanitities, whose rows correspond to statistical units, β is a p×1 vector of (unobservable) parameters, and ε is an n×1 vector of "errors", which are uncorrelated random variables each with expected value 0 and variance σ². Often one takes the components of the vector of errors to be independent and normally distributed. Having observed the values of X and Y, the statistician must estimate β and σ². Typically the parameters β are estimated by the method of least squares.

If, rather than taking the variance of ε to be σ²I, where I is the n×n identity matrix, one assumes the variance is σ²M, where M is a known matrix other than the identity matrix, then one estimates β by the method of "generalized least squares", in which, instead of minimizing the sum of squares of the residuals, one minimizes a different quadratic form in the residuals — the quadratic form being the one given by the matrix M^-1. If all of the off-diagonal entries in the matrix M are 0, then one normally estimates β by the method of "weighted least squares", with weights proportional to the reciprocals of the diagonal entries.

Ordinary Linear regression is a very closely related topic.

1 Generalizations

1.1 Generalized linear models

• E(Y)=Xβ,

say

• f(E(Y))=Xβ,

where f is the "link function". An example is the "Poisson regression model", which says

• Y_i has a Poisson distribution with expected value e^γ+δx_i.

The link function is the natural logarithm function. Having observed x_i and Y_i for i=1,...,n, one can estimate γ and δ by the method of maximum likelihood.

1.2 General linear model

The general linear model (or multivariate regression model ) is a linear model with multiple measurements per object. Each object may be represented in a vector.