Summary
Consider ap-variate random vectorX=(X 1,…,X p ),p≧2, with an unknown meanμ ∈ R p and an unknown dispersion matrix σ(p.d.). ForX (2) =(X 2,…,X p )′, the regression ofX 1 onX (2) is defined asE(X 1 |X (2). The multiple correlation coefficient betweenX 1 andX (2), denoted by ρ1·23...p , is the simple correlation coefficient betweenX 1 and its best linear fitX 1·23...p =β 1+β 2 X 2+...β p X p byX (2) whereβ i ’s are regression coefficients. The parameter λ=ρ 2 1·23...p is called the coefficient of multiple determination and it indicates the extent of the true contribution of the explanatory variablesX 2…X p in explaining the response variableX 1 through a linear regression model. In this article we address the problem of efficient estimation of λ under the Pitman Nearness Criterion (PNC) as well as the Stochastic Domination Criterion (SDC) assuming thatX follows a multivariate normal distribution (N p (μ, σ)). We have proposed several competing shrinkage estimators which seem to outperform the usual maximum likelihood estimator by wide margins. Finally, simulation results and two real life data sets (from environmental studies) have been used to demonstrate the effectiveness of our proposed estimators.
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References
Ghosh, J. K. andSinha, B. K. (1981), A necessary and sufficient condition of second order admissibility with applications to Berkson’s bioassay problem.The Annals of Statistics, 9, 1334–1338.
HARE, C. T. and BRADOW, R. L. (1977), Light duty diesel emission correction factors for ambient conditions, Paper # 770717, Society of automotive engineers off-highway vehicle meeting, MECCA Milwaukee, September 12–15, 1977.
Hwang, J. T. (1985), Universal domination and stochastic domination: estimation simultaneously under a broad class of loss functions.The Annals of Statistics, 13, 295314.
Johnson, R. A. andWichern, D. W.(1988), Applied multivariate statistical analysis. Englewood Cliffs, NJ: Prentice-Hall.
Mardia, K. V. (1980), Tests for univariate and multivariate normality. Handbook of statistics, Vol. 1: Analysis of variance, (ed. P. R. Krishnaiah), 279–320. North Holland, Amsterdam.
Mcdonald, G. C. andSchwing, R. C. (1973), Instabilities of regression estimates relating air pollution to mortality.Technometrics, 15, 463–482.
Olkin, I. andPrati, J. W. (1958), Unbiased estimation of certain correlation coefficient.The Annals of Mathematical Statistics, 29, 201–211.
Pal, N. andLim, W. K. (1998), Estimation of the coefficient of multiple determination.Annals of the Institute of Statistical Mathematics, vol. 50, 4, 773–778.
Pal, N. andLim, W. K. (1999), Shrinkage estimation of a correlation coefficient and two examples with real life data sets.Journal of Statistical Computation & Simulation, 62, 357–373.
Pal, N. andLim, W. K. (2000), Estimation of a correlation coefficient: some second order decision-theoretic results.Statistics & Decisions, 18, 185–203.
Rao, C. R. (1980), Discussion of J. Berkson’s paper «Minimum Chi-square, not maximum likelihood».The Annals of Statistics, 8, 482–485.
RAO C. R. (1981), Some comments on the minimum mean square error as a criterion of estimation. InStatistics and related topics (M. Csörgo, D. Dawson, J. Rao, A. Saleh eds.), 123–143.
Rao, C. R., Keating, J. andMason, R. (1986), The Pitman Nearness Criterion and its determination.Communication in Statistics, Theory & Methods, 15, 3173–3191.
STEIN, C. (1956), Inadmissibility of the usual estimator for the mean of a multivariate distribution.Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability, ed. Jerzy Neyman, 1, 197–206. Berkeley.
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Pal, N., Lim, W.K. On the coefficient of multiple determination in a linear regression model. J. Ital. Statist. Soc. 7, 129–157 (1998). https://doi.org/10.1007/BF03178925
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DOI: https://doi.org/10.1007/BF03178925