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MINQUE
In statistics, the theory of minimum norm quadratic unbiased estimation (MINQUE)[1][2][3] was developed by C. R. Rao. MINQUE is a theory alongside other estimation methods in estimation theory, such as the method of moments or maximum likelihood estimation. Similar to the theory of best linear unbiased estimation, MINQUE is specifically concerned with linear regression models.[1] The method was originally conceived to estimate heteroscedastic error variance in multiple linear regression.[1] MINQUE estimators also provide an alternative to maximum likelihood estimators or restricted maximum likelihood estimators for variance components in mixed effects models.[3] MINQUE estimators are quadratic forms of the response variable and are used to estimate a linear function of the variances.
- ^ a b c Rao, C.R. (1970). "Estimation of heteroscedastic variances in linear models". Journal of the American Statistical Association. 65 (329): 161–172. doi:10.1080/01621459.1970.10481070. JSTOR 2283583.
- ^ Rao, C.R. (1971). "Estimation of variance and covariance components MINQUE theory". J Multivar Anal. 1 (3): 257–275. doi:10.1016/0047-259x(71)90001-7. hdl:10338.dmlcz/104230.
- ^ a b Rao, C.R. (1972). "Estimation of variance and covariance components in linear models". Journal of the American Statistical Association. 67 (337): 112–115. doi:10.1080/01621459.1972.10481212. JSTOR 2284708.
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