MINQUE

In statistics, the theory of minimum norm quadratic unbiased estimation (MINQUE)^[1][2][3] was developed by C.R. Rao. Its application was originally to the estimation of variance components in random effects models.

The theory involves three stages:

• defining a general class of potential estimators as quadratic functions of the observed data, where the estimators relate to a vector of model parameters;
• specifying certain constraints on the desired properties of the estimators, such as unbiasedness;
• choosing the optimal estimator by minimising a "norm" which measures the size of the covariance matrix of the estimators.

References

1. ^ Rao, C.R. (1970). "Estimation of heteroscedastic variances in linear models". J Am Stat Assoc 65 (329): 161–172. JSTOR 2283583. 
2. ^ Rao, C.R. (1971). "Estimation of variance and covariance components MINQUE theory". J Multivar Anal 1: 257–275. 
3. ^ Rao, C.R. (1972). "Estimation of variance and covariance components in linear models". J Am Stat Assoc 67: 112–115. JSTOR 2284708.