A Nonparametric Way of Distribution Testing
AbstractTesting the distribution of a random sample can be considered ,indeed, as a goodness-of-fit problem. If we use the nonparametric density estimation of the sample as a consistent estimate of exact distribution, the problem reduces, more specifically, to the distance of two functions. This paper examines the distribution testing from this point of view and suggests a nonparametric procedure. Although the procedure is applicable for all distributions, paper emphasizes on normality test.The critical values for this normality test generated by using Monte Carlo techniques.
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Bibliographic InfoPaper provided by EconWPA in its series Econometrics with number 0510006.
Length: 22 pages
Date of creation: 29 Oct 2005
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distribution testing; normality; monte carlo simulation;
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- Thanasis Stengos & Ximing Wu, 2006.
"Information-Theoretic Distribution Test with Application to Normality,"
Working Papers, University of Guelph, Department of Economics and Finance
0604, University of Guelph, Department of Economics and Finance.
- Thanasis Stengos & Ximing Wu, 2010. "Information-Theoretic Distribution Test with Application to Normality," Econometric Reviews, Taylor & Francis Journals, vol. 29(3), pages 307-329.
- Thanasis Stengos & Ximing Wu, 2006. "Information-Theoretic Distribution Test with Application to Normality," University of Cyprus Working Papers in Economics 3-2006, University of Cyprus Department of Economics.
- Jarque, Carlos M. & Bera, Anil K., 1980. "Efficient tests for normality, homoscedasticity and serial independence of regression residuals," Economics Letters, Elsevier, vol. 6(3), pages 255-259.
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