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inultivariate general linear Gauss-Markoff model; Wishart distribution; multinormal distribution; set of linear estimable parametric functions; quadratic form; singular covariance matrix
This paper concerns generalized quadratic forms for the multivariate case. These forms are used to test linear hypotheses of parameters for the multivariate Gauss-Markoff model with singular covariance matrix. Distributions and independence of these forms are proved.
[1] Mardia K. V., Kent J. T., Bibby J. M.: Multivariate Analysis. Academic Press, London, 1979. MR 0560319 | Zbl 0432.62029
[2] Oktaba W.: Estimation of the parametric functions in the multivariate general Gauss-Markoff model. XV Colloquium Metodol., Agrobiom., Pol. Acad. Sci., Warsaw, 1985, pp. 178-182. (In Polish.)
[3] Rao C. R.: Linear Statistical Inference and Its Applications. sec. ed., J. Wiley, New York, 1973. MR 0346957 | Zbl 0256.62002
[4] Rao C. R., Mitra S. K.: Generalized Inverse of Matrices and its Applications. J. Wiley, New York, 1971. MR 0338013 | Zbl 0236.15005
[5] Roy S. N.: Some aspects of multivariate analysis. Indian Statistical Institute, New York, Calcutta, 1957. MR 0092296
[6] Seber G. А. F.: Multivariate Observations. J. Wiley, New York, 1984. MR 0746474 | Zbl 0627.62052
[7] Srivastava M. S., Khatri C. G.: An Introduction to Multivariate Statistics. Elsevier North Holland, Inc., New York, 1979. MR 0544670 | Zbl 0421.62034
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