Skip to main content
Log in

On exact D-optimal designs for regression models with correlated observations

  • Regression
  • Published:
Annals of the Institute of Statistical Mathematics Aims and scope Submit manuscript

Abstract

Let τ* be an exact D-optimal design for a given regression model Y τ = X τβ + Z τ. In this paper sufficient conditions are given for sesigning how the covariance matrix of Z τ may be changed so that not only τ* remains D-optimal but also that the best linear unbiased estimator (BLUE) of β stays fixed for the design τ*, although the covariance matrix of Z τ* is changed. Hence under these conditions a best, according to D-optimality, BLUE of β is known for the model with the changed covariance matrix. The results may also be considered as determination of exact D-optimal designs for regression models with special correlated observations where the covariance matrices are not fully known. Various examples are given, especially for regression with intercept term, polynomial regression, and straight-line regression. A real example in electrocardiography is treated shortly.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Bickel, P. J. and Herzberg, A. M. (1979). Robustness of design against autocorrelation in time I: asymptotic theory, optimality for location and linear regression, Ann. Statist., 7, 77–95.

    Google Scholar 

  • Bickel, P. J., Herzberg, A. M. and Schilling, M. (1981). Robustness of design against autocorrelation in time II: optimality, theoretical and numerical results for the first-order-autoregressive process, J. Amer. Statist. Assoc., 76, 870–877.

    Google Scholar 

  • Bischoff, W. (1988). Über konkrete D-optimale Versuchspläne für die Geradenregression, Docteral Thesis, University of Karlsruhe.

  • Bischoff, W., Cremers, H. and Fieger, W. (1987). Optimal regression models in electrocardiography, Innovation et Technologie en Biologie et Medcine, 8, 24–34.

    Google Scholar 

  • Budde, M. (1984). Optimale Zweifachblockpläne bei seriell korrelierten Fehlern, Metrika, 31, 203–213.

    Google Scholar 

  • Cramér, H. (1963). Mathematical Methods of Statistics, Princeton University Press, New Jersey.

    Google Scholar 

  • Federov, V. V. (1972). Theory of Optimal Experiments, Academic Press, New York.

    Google Scholar 

  • Gaffke, N. (1987). On D-optimality of exact linear regression designs with minimum support, J. Statist. Plann. Inference, 15, 189–204.

    Google Scholar 

  • Graybill, F. A. (1983). Matrices with Applications in Statistics, Wadsworth, Belmont, California.

    Google Scholar 

  • Haberman, S. J. (1975). How much do Gauss-Markov and least squares estimates differ? A coordinate-free approach, Ann. Statist., 3, 982–990.

    Google Scholar 

  • Karlin, S. and Studden, W. J. (1966). Optimal experimental designs, Ann. Math. Statist., 37, 783–815.

    Google Scholar 

  • Kiefer, J. and Wynn, H. P. (1981). Optimum balanced block and Latin square designs for correlated observations, Ann. Statist., 9, 737–757.

    Google Scholar 

  • Kruskal, W. (1968). When are Gauss-Markov and least squares estimators identical? A coordinate-free approach, Ann. Math. Statist., 39, 70–75.

    Google Scholar 

  • Kunert, J. and Martin, R. J. (1987). On the optimality of finite Williams II(a) designs, Ann. Statist., 15, 1604–1628.

    Google Scholar 

  • Näther, W. (1985). Exact designs for regression models with correlated errors, Statistics, 16, 479–484.

    Google Scholar 

  • Sacks, J. and Ylvisaker, D. (1966). Design for regression problems with correlated errors, Ann. Math. Statist., 37, 66–89.

    Google Scholar 

  • Sacks, J. and Ylvisaker, D. (1968). Design for regression problems with correlated errors: many parameters, Ann. Math. Statist., 39, 49–69.

    Google Scholar 

  • Sacks, J. and Ylvisaker, D. (1970). Designs for regression problems with correlated errors III, Ann. Math. Statist., 41, 2057–2074.

    Google Scholar 

  • Zyskind, G. (1967). On canonical forms non-negative covariance matrices and best and simple least squares linear estimators in linear models, Ann. Math. Statist., 38, 1092–1109.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

About this article

Cite this article

Bischoff, W. On exact D-optimal designs for regression models with correlated observations. Ann Inst Stat Math 44, 229–238 (1992). https://doi.org/10.1007/BF00058638

Download citation

  • Received:

  • Revised:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF00058638

Key words and phrases

Navigation