Predictive performance of the methods of restricted and mixed regression estimators

Biometrical Journal

Volumn 38, Issue 8, 1996, Pages 951-959

  Toutenburg, H ^a   Shalabh, ^b  

^a UNIVERSITY OF MUNICH   (Germany)

^b UNIVERSITY OF JAMMU   (India)

Author keywords

[No Author keywords available]

Indexed keywords

LINEAR REGRESSION;

AVERAGE VALUES; LEAST SQUARE; LINEAR REGRESSION MODELLING; PERFORMANCE PROPERTIES; PREDICTIVE PERFORMANCE; REGRESSION COEFFICIENT; REGRESSION ESTIMATORS;

LEAST SQUARES APPROXIMATIONS;

EID: 0039270410     PISSN: 03233847     EISSN: None     Source Type: Journal    
DOI: 10.1002/bimj.4710380807     Document Type: Article

References (7)

1. KAKWANI, N. C., 1968: Note on the unbiasedness of mixed regression estimation. Econometrica 36, 610-611.
2. RAO, C. R. and TOUTENBURG, H., 1995: Linear Models: Least Squares and Alternatives. Springer, New York.
3. SHALABH, 1995: Performance of Stein-rule procedure for simultaneous prediction of actual and average values of study variable in linear regression model. Bulletin of the International Statistical Institute 56, 1375-1390.
4. SWAMY, P. A. V. B. and MEHTA, J. S., 1969: On Theil's mixed regression estimator. Journal of the American Statistical Association 66, 273-276.
5. THEIL, H. and GOLDBERGER, A. S., 1961: On pure and mixed estimation in econometrics. International Economic Review 2, 65-78.
6. TOUTENBURG, H. and TRENKLER, G., 1990: Mean square error matrix comparisons of optimal and classical predictors and estimators in linear regression. Computational Statistics and Data Analysis 10, 297-305.
7. TOUTENBURG, H., TRENKLER, G., and LISKI, E. P., 1992: Optimal erstimation methods under weakened linear restrictions in regression. Computational Statistics and Data Analysis 14, 527-536.