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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2003.12
- 수록면
- 1,013 - 1,021 (9page)
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Huffer and Park(2002) proposed a chi-squared test for multivariate structure. Their test detects the deviation of data from mutual independence or multivariate normality. We will compute the Rao-Roboson chi-squared version of the test, which is easy to apply in practice since it has a limiting chi-squared distribution. We will provide a self-contained argument that it has a limiting chi-squared distribution. We study the accuracy in finite samples of the limiting distribution. We finally compare the power of our test with those of other popular normality tests in an application to a real data.
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UCI(KEPA) : I410-ECN-0101-2018-041-001391715