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Finite-sample inference with monotone incomplete multivariate normal data, II

Wan-Ying Chang and Donald St. P. Richards

Journal of Multivariate Analysis, 2010, vol. 101, issue 3, 603-620

Abstract: We continue our recent work on inference with two-step, monotone incomplete data from a multivariate normal population with mean and covariance matrix . Under the assumption that is block-diagonal when partitioned according to the two-step pattern, we derive the distributions of the diagonal blocks of and of the estimated regression matrix, . We represent in terms of independent matrices; derive its exact distribution, thereby generalizing the Wishart distribution to the setting of monotone incomplete data; and obtain saddlepoint approximations for the distributions of and its partial Iwasawa coordinates. We prove the unbiasedness of a modified likelihood ratio criterion for testing , where is a given matrix, and obtain the null and non-null distributions of the test statistic. In testing , where and are given, we prove that the likelihood ratio criterion is unbiased and obtain its null and non-null distributions. For the sphericity test, , we obtain the null distribution of the likelihood ratio criterion. In testing we show that a modified locally most powerful invariant statistic has the same distribution as a Bartlett-Pillai-Nanda trace statistic in multivariate analysis of variance.

Keywords: Likelihood; ratio; tests; Locally; most; powerful; invariant; tests; Matrix; F-distribution; Maximum; likelihood; estimation; Missing; completely; at; random; Multivariate; analysis; of; variance; Testing; independence; Sphericity; test; Unbiased; test; statistics; Wishart; distribution (search for similar items in EconPapers)
Date: 2010
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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