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Significance Testing in Exact Logistic Multiple Regression

Mezbahur Rahman, and Chakrobartty, Shuvro, (2004) Significance Testing in Exact Logistic Multiple Regression. Bulletin of the Malaysian Mathematical Sciences Society, 27 (2). pp. 207-215. ISSN 0126-6705

Full text not available from this repository.

Official URL: http://math.usm.my/bulletin/pdf/v27n2/v27n2p10.pdf

Affiliations

Minnesota State University

Abstract

Exact logistic regression is discussed for multiple regressors. The exact significance of a regressor is computed which can be used in simplifying the model and/or to compute the significance of a variable or a set of variables in the model. Bilingual education data is analyzed using the procedure mentioned in this paper.

Item Type:Journal
Keywords:Binary response model; Discriminant analysis; Goodness-of-fit; Least square estimate; Maximum likelihood estimate.
Subjects:Q Science, Computer Science
ID Code:1308

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2. D.W. Hosmer Jr. and S. Lemeshow, Applied Logistic Regression, Second edition. New York: Wiley, 2000.

3. E.L. Lehmann, Testing Statistical Hypothesis, New York: John Wiley, 1995.

4. R.C. Mehta, N.R. Patel, and P. Senchaudhuri, Efficient Monte Carlo methods for conditional logistic regression, Journal of the American Statistical Association 95 (449) (2000), 99—108.

5. D.A. Powers, Statistical Methods for Categorical Data Analysis, London, UK: Academic Press, 2000.

6. T.P. Ryan, Modern Regression Methods, New York: Wiley, 1997.

7. R.C. Singleton, An algorithm for computing the mixed-radix fast Fourier transformation, IEEE Transactions on Audio and Electroacoustics 17 (1969), 93—103.

8. D. Tritchler, An algorithm for exact logistic regression, Journal of the American Statistical Association 79 (1984), 709—711.

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