Maximum likelihood estimation with incomplete multinomial data /

Bibliographic Details
Main Authors:	Hocking, R. R. (Ronald R.), 1932- (Author), Oxspring, H. H. (Author)
Corporate Authors:	United States. Office of Naval Research (sponsoring body.), United States. Department of Defense (sponsoring body.), Texas A & M Research Foundation
Format:	Book
Language:	English
Published:	[College Station, Tex.] : Institute of Statistics, Texas A & M University, 1971.
Series:	Technical report (Texas A & M University. Themis Optimization Research Program) ; no. 31.
Subjects:	Parameter estimation. Estimation theory. Sampling (Statistics) Iterative methods (Mathematics) Mathematical optimization. Multinomial distribution Incomplete data

Description
Abstract:	When sampling from a multinomial population, it may happen either by chance or by design that some of the observations are only partially classified. In this paper the amount of information in the partially classified observation is evaluated, and maximum likelihood estimators are obtained using both completely classified and partially classified data. In general an iterative solution is required, but special cases are identified in which the solution is obtained in closed form.
Item Description:	"August 1971." "Research conducted through the Texas A & M Research Foundation." Additional funding information taken from leaf [1].
Physical Description:	20 leaves, 8 unnumbered leaves ; 28 cm
Bibliography:	Includes bibliographical references (leaf 20).