On a Unified Theory of Estimation in Linear Models
Abstract
In a series of papers the author developed two approaches towards a unified treatment of the General Gauss-Markoff (GGM) linear model (Y, X beta, sigma squared V) where V, the dispersion matrix of Y, may be singular and X may be deficient in rank. One is called the inverse partition (IPM) method which depends on the numerical evaluation of a g-inverse of a partitioned matrix. Another is an analogue of least square theory and is called unified least square (ULS) method. The aim of the paper is to bring out the salient features of these two methods and to point out some interesting features of linear unbiased estimation when the dispersion matrix of the observations is singular.
Document Details
- Document Type
- Technical Report
- Publication Date
- May 01, 1973
- Accession Number
- AD0763404
Entities
People
- Calyampudi Radhakrishna Rao
Organizations
- Purdue University