On the Power of Certain Tests for Independence in Bivariate Populations

Konijn, H. S.

doi:10.1214/aoms/1177728260

Cited by 60 publications

(21 citation statements)

References 6 publications

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“…Fieller, Hartley & Pearson (1957) considered (X, Y) distributions obtained by the method of translation from the normal model and Konijn (1956) studied the class of distributions generated from linear combinations of two independent variables. Fieller, Hartley & Pearson (1957) considered (X, Y) distributions obtained by the method of translation from the normal model and Konijn (1956) studied the class of distributions generated from linear combinations of two independent variables.…”

Section: Other Approaches To the Analysis Of Associationmentioning

confidence: 99%

Co-Ordinate Transformations to Normality and the Power of Normal Tests for Independence

Kowalski

Tarter

1969

Biometrika

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JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. Biometrika Trust is collaborating with JSTOR to digitize, preserve and extend access to Biometrika. SUMMARY This paper considers properties of the normal theory test for independence applied to observations from bivariate distributions constrained to have normal marginals. Coordinate transformations to normality are applied to a variety of non-normal distributions (X, Y) and it is found that the normal theory test for independence is more powerful when applied to the transformed vector than when (X, Y) is used. The general transformation of an arbitrary continuous variate to standard normality is discussed and a method for obtaining an accurate estimator of this transformation is presented. The method is applied to several sets of real data and improvement is demonstrated in terms of agreement with previously established theory.

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Section: Other Approaches To the Analysis Of Associationmentioning

confidence: 99%

Co-Ordinate Transformations to Normality and the Power of Normal Tests for Independence

Kowalski

Tarter

1969

Biometrika

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“…2, e(r,, r) approaches infinity. Konijn (1956) has obtained the ARE of r8 relative to r, e*(r,, r), for the family derived from linear combinations of two independent variables and are shown in Table 1 for comparison. It is interesting that e(r,, r) > 1 for the c-type population, whereas e*(r8, r) can take values less than 1, and e*(r8, r) is less than e(r,, r) for the cases considered in Table 1.…”

Section: Are's Of Some Tests Of Independencementioning

confidence: 99%

The Performance of Some Tests of Independnece for Contingency-Type Bivariate Distributions

Mardia¹

1969

Biometrika

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“…The asymptotic relative efficiencies of the tests r, r,, r and T.(') have been studied in [9,2,12] and others. Except for r, no information seems to be available about their performance in small and moderate sample sizes.…”

Section: Introductionmentioning

confidence: 99%

Percentage Points of Some Non-Parametric Tests for Independence and Empirical Power Comparisons

Bhattacharyya

Johnson²,

Neave

1970

Journal of the American Statistical Association

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On the Power of Certain Tests for Independence in Bivariate Populations

Cited by 60 publications

References 6 publications

Co-Ordinate Transformations to Normality and the Power of Normal Tests for Independence

Co-Ordinate Transformations to Normality and the Power of Normal Tests for Independence

The Performance of Some Tests of Independnece for Contingency-Type Bivariate Distributions

Percentage Points of Some Non-Parametric Tests for Independence and Empirical Power Comparisons

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