Estimation of missing data in analysis of covariance: A least-squares approach

被引：3

作者：

Ogbonnaya, Chibueze E. ^{[1
]}

Uzochukwu, Emeka C. ^{[1
]}

机构：

[1] Univ Nigeria, Fac Phys Sci, Dept Stat, Nsukka, Enugu, Nigeria

来源：

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS | 2016年 / 45卷 / 07期

关键词：

Analysis of covariance; Covariate; Dependent variable; Error sum of squares; Least squares; Missing data; 62J10; 62K99; VALUES; PLOT;

D O I：

10.1080/03610926.2013.868000

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

A method is proposed for the estimation of missing data in analysis of covariance models. This is based on obtaining an estimate of the missing observation that minimizes the error sum of squares. Specific derivation of this estimate is carried out for the one-factor analysis of covariance, and numerical examples are given to show the nature of the estimates produced. Parameter estimates of the imputed data are then compared with those of the incomplete data.

引用

页码：1902 / 1909

页数：8

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