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Gauss-Markov theorem


A Gauss-Markov linear model is a linear statistical model that satisfies all the conditions of a general linear model except the normality of the error terms. Formally, if 𝒀 is an m-dimensional response variable vector, and 𝒁𝒊=zi(𝑿), i=1,,k are the m-dimensional functions of the explanatory variable vector 𝑿, a Gauss-Markov linear model has the form:

𝒀=β0𝒁𝟎++βk𝒁𝒌+ϵ,

with ϵ the error vector such that

  1. 1.

    E[ϵ]=𝟎, and

  2. 2.

    Var[ϵ]=σ2𝑰.

In other words, the observed responses Yi, i=1,,m are not assumed to be normally distributed, are not correlated with one another, and have a common varianceMathworldPlanetmath Var[Yi]=σ2.

Gauss-Markov Theorem. Suppose the response variable 𝒀=(Y1,,Ym) and the explanatory variables 𝑿 satisfy a Gauss-Markov linear model as described above. Consider any linear combinationMathworldPlanetmath of the responses

Y=mi=1ciYi, (1)

where ci. If each μi is an estimatorMathworldPlanetmath for response Yi, parameter θ of the form

θ=mi=1ciμi, (2)

can be used as an estimator for Y. Then, among all unbiased estimatorsMathworldPlanetmath for Y having form (2), the ordinary least square estimator (OLS)

ˆθ=mi=1ci^μi, (3)

yields the smallest variance. In other words, the OLS estimator is the uniformly minimum variance unbiased estimator.

Remark. ˆθ in equation (3) above is more popularly known as the BLUE, or the best linear unbiased estimator for a linear combination of the responses in a Gauss-Markov linear model.

Title Gauss-Markov theorem
Canonical name GaussMarkovTheorem
Date of creation 2013-03-22 15:02:53
Last modified on 2013-03-22 15:02:53
Owner CWoo (3771)
Last modified by CWoo (3771)
Numerical id 8
Author CWoo (3771)
Entry type Theorem
Classification msc 62J05
Synonym BLUE
Related topic LinearLeastSquaresFit
Defines Gauss-Markov linear model
Defines best linear unbiased estimator