Improved Multivariate Prediction in a General Linear Model with an Unknown Error Covariance Matrix

This paper deals with the problem of Stein-rule prediction in a general linear model. Our study extends the work of Gotway and Cressie (1993) by assuming that the covariance matrix of the model's disturbances is unknown. Also, predictions are based on a composite target function that incorporat...

Full description

Saved in:
Bibliographic Details
Published in:Journal of multivariate analysis 2002-10, Vol.83 (1), p.166-182
Main Authors: Chaturvedi, Anoop, Wan, Alan T.K., Singh, Shri P.
Format: Article
Language:English
Subjects:
Exact sciences and technology
large sample asymptotic
large sample asymptotic prediction quadratic loss risk Stein-rule
Linear inference, regression
Mathematics
prediction
Probability and statistics
quadratic loss
risk
Sciences and techniques of general use
Statistics
Stein-rule
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper deals with the problem of Stein-rule prediction in a general linear model. Our study extends the work of Gotway and Cressie (1993) by assuming that the covariance matrix of the model's disturbances is unknown. Also, predictions are based on a composite target function that incorporates allowance for the simultaneous predictions of the actual and average values of the target variable. We employ large sample asymptotic theory and derive and compare expressions for the bias vectors, mean squared error matrices, and risks based on a quadratic loss structure of the Stein-rule and the feasible best linear unbiased predictors. The results are applied to a model with first order autoregressive disturbances. Moreover, a Monte-Carlo experiment is conducted to explore the performance of the predictors in finite samples.
ISSN:0047-259X
1095-7243
DOI:10.1006/jmva.2001.2042