Some results in finding a lower bound of the efficiency of least square estimates relative to best linear estimates in regression model

dc.contributor.author	Raghavachari, M.
dc.date.accessioned	2010-03-15T04:32:48Z
dc.date.available	2010-03-15T04:32:48Z
dc.date.copyright	1974-12
dc.date.issued	2010-03-15T04:32:48Z
dc.identifier.uri	http://hdl.handle.net/11718/1297
dc.description.abstract	Consider the usual regression model Y = X? + ?. The standard estimators of ? are (i) Least squares estimator and (ii) Best linear estimator. The paper gives some results on finding an attainable lower bound on the efficiency of least square estimates relative to the best linear estimate. Specifically the paper is an attempt to verify the validity of a conjecture made by G.S. Watson. Consider the usual regression model Y = X? + ?. The standard estimators of ? are (i) Least squares estimator and (ii) Best linear estimator. The paper gives some results on finding an attainable lower bound on the efficiency of least square estimates relative to the best linear estimate. Specifically the paper is an attempt to verify the validity of a conjecture made by G.S. Watson.	en
dc.language.iso	en	en
dc.relation.ispartofseries	WP;1974/62
dc.subject	least square estimates	en
dc.subject	regression model
dc.title	Some results in finding a lower bound of the efficiency of least square estimates relative to best linear estimates in regression model	en
dc.type	Working Paper	en