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.
E[ϵ]=𝟎, and
-
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 variance
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 combination of the responses
Y=m∑i=1ciYi, | (1) |
where ci∈ℝ. If each μi is an estimator for response Yi, parameter θ of the form
θ=m∑i=1ciμi, | (2) |
can be used as an estimator for Y. Then, among all unbiased estimators for Y having form (2), the ordinary least square estimator (OLS)
ˆθ=m∑i=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 |