Distribution of the `Generalised' Multiple Correlation Matrix in the Dual Case

Khatri, C. G.

doi:10.1214/aoms/1177700402

Cited by 18 publications

(5 citation statements)

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“…In the bivariate case it is obvious that (2) is again the required distribution. Khatri [10] has shown that in the multivariate case (2) continues to be applicable.…”

Section: The Distribution and Moments Of The Largest Squared Correlatmentioning

confidence: 99%

Some Evidence on the Largest Squared Correlation Coefficient from Several Samples

Bacon¹

1977

Econometrica

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“…In the bivariate case it is obvious that (2) is again the required distribution. Khatri [10] has shown that in the multivariate case (2) continues to be applicable.…”

Section: The Distribution and Moments Of The Largest Squared Correlatmentioning

confidence: 99%

Some Evidence on the Largest Squared Correlation Coefficient from Several Samples

Bacon¹

1977

Econometrica

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“…The distributions of these statistics and their independence have not been explicitly derived so far. The symmetry or "duality" in the relationship of x and y, in the absence of any true association, has been noted by several people (Fisher, 1936;Bartlett, 1947;Khatri, 1964). Bartlett's derivation is based on a geometrical argument and deals with the case when the proposed discriminant function is from the x-space.…”

Section: Introductionmentioning

confidence: 97%

Goodness of Fit of a Discriminant Function from the Vector Space of Dummy Variables

Kshirsagar

1971

Journal of the Royal Statistical Society Series B: Statistical Methodology

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Summary The problem of discrimination among several groups can be looked upon as the problem of “association” between the vector of variables under consideration and a vector of “dummy” variables representing the contrasts among the groups. In certain situations, observations on one or more of these dummy variables are explicitly available and tests have been derived by Bartlett and Williams, for the adequacy of a set of assigned discriminant functions from among these dummy variables. In this paper, the statistical distributions of these test criteria are derived analytically, assuming the null hypothesis to be true.

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“…Several problems such as multivariate analysis of variance and canonical correlation analysis are associated with R of (1.1). An enumeration of such problems may be seen from Khatri [3]. is done in Khatri [3] by using a rather lengthy procedure.…”

Section: Introductionmentioning

confidence: 99%

“…An enumeration of such problems may be seen from Khatri [3]. is done in Khatri [3] by using a rather lengthy procedure. Using more or less the same approach an extension of Khatri's result is given in Srivastava [7].…”

Section: Introductionmentioning

confidence: 99%

Distribution of the canonical correlation matrix

Mathai

1981

Ann Inst Stat Math

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SummaryGeneralized canonical correlation matrix is associated with canonical correlation analysis, multivariate analysis of variance, a large variety of statistical tests and regression problems. In this paper two methods of deriving the distribution are given and the exact distribution is given in an elegant form. The techniques of derivation are applicable to all versions of the generalized canonical correlation matrices, nonnull distributions in generalized analysis of variance problems and also they give rise to a simpler derivation of the distribution of the multiple correlation coefficient.

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Distribution of the `Generalised' Multiple Correlation Matrix in the Dual Case

Cited by 18 publications

References 0 publications

Some Evidence on the Largest Squared Correlation Coefficient from Several Samples

Some Evidence on the Largest Squared Correlation Coefficient from Several Samples

Goodness of Fit of a Discriminant Function from the Vector Space of Dummy Variables

Distribution of the canonical correlation matrix

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