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Multivariate Linear Models: General

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Advanced Linear Modeling

Part of the book series: Springer Texts in Statistics ((STS))

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Abstract

This chapter introduces the basic theory for linear models with more than one dependent variable.

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References

  • Anderson, T. W. (2003). An introduction to multivariate statistical analysis (3rd ed.). New York: Wiley.

    MATH  Google Scholar 

  • Arnold, S. F. (1981). The theory of linear models and multivariate analysis. New York: Wiley.

    MATH  Google Scholar 

  • Christensen, R. (1996). Analysis of variance, design, and regression: Applied statistical methods. London: Chapman and Hall.

    MATH  Google Scholar 

  • Christensen, R. (2011). Plane answers to complex questions: The theory of linear models (4th ed.). New York: Springer.

    Book  Google Scholar 

  • Dillon, W. R., & Goldstein, M. (1984). Multivariate analysis: Methods and applications. New York: Wiley.

    MATH  Google Scholar 

  • Eaton, M. L. (1983). Multivariate statistics: A vector space approach. New York: Wiley.

    MATH  Google Scholar 

  • Everitt, B., & Hothorn, T. (2011). An introduction to applied multivariate analysis with R. New York: Springer.

    Book  Google Scholar 

  • Gnanadesikan, R. (1977). Methods for statistical data analysis of multivariate observations. New York: Wiley.

    MATH  Google Scholar 

  • Heck, D. L. (1960). Charts of some upper percentage points of the distribution of the largest characteristic root. Annals of Mathematical Statistics, 31, 625–642.

    Article  MathSciNet  Google Scholar 

  • Johnson, R. A., & Wichern, D. W. (2007). Applied multivariate statistical analysis (6th ed.). Englewood Cliffs, NJ: Prentice-Hall

    MATH  Google Scholar 

  • Kres, H. (1983). Statistical tables for multivariate analysis. New York: Springer.

    Book  Google Scholar 

  • McKeon, J. J. (1974). F approximations to the distribution of Hotelling’s \(T_0^2\). Biometrika, 61, 381–383.

    MathSciNet  MATH  Google Scholar 

  • Marden, J. I. (2015). Multivariate statistics: Old school. http://stat.istics.net/Multivariate

    Google Scholar 

  • Mardia, K. V., Kent, J. T., & Bibby, J. M. (1979). Multivariate analysis. New York: Academic.

    MATH  Google Scholar 

  • Morrison, D. F. (2004). Multivariate statistical methods (4th ed.). Pacific Grove, CA: Duxbury Press.

    Google Scholar 

  • Muirhead, R. J. (1982). Aspects of multivariate statistical theory. New York: Wiley.

    Book  Google Scholar 

  • Okamoto, M. (1973). Distinctness of the eigenvalues of a quadratic form in a multivariate sample. Annals of Statistics, 1, 763–765.

    Article  MathSciNet  Google Scholar 

  • Press, S. J. (1982). Applied multivariate analysis: Using Bayesian and frequentist methods of inference (2nd ed.). Malabar, FL: R.E. Krieger (Latest reprinting, Dover Press, 2005).

    Google Scholar 

  • Rao, C. R. (1951). An asymptotic expansion of the distribution of Wilks’ criterion. Bulletin of the International Statistical Institute, 33, 177–180.

    MathSciNet  MATH  Google Scholar 

  • Rencher, A. C., & Christensen, W. F. (2012). Methods of multivariate analysis (3rd ed.). New York: Wiley.

    Book  Google Scholar 

  • Roy, S. N. (1953). On a heuristic method of test construction and its use in multivariate analysis. Annals of Mathematical Statistics, 24, 220–238.

    Article  MathSciNet  Google Scholar 

  • Roy, S. N., & Bose, R. C. (1953). Simultaneous confidence interval estimation. Annals of Mathematical Statistics, 24, 513–536.

    Article  MathSciNet  Google Scholar 

  • Seber, G. A. F. (1984). Multivariate observations. New York: Wiley.

    Book  Google Scholar 

  • Tarpey, T., Ogden, R. T., Petkova, E., & Christensen, R. (2015). A paradoxical result in estimating regression coefficients. The American Statistician, 68, 271–276.

    Article  MathSciNet  Google Scholar 

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Christensen, R. (2019). Multivariate Linear Models: General. In: Advanced Linear Modeling. Springer Texts in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-030-29164-8_9

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