A Nonparametric Way of Distribution Testing
Testing 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.
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Pagan,Adrian & Ullah,Aman, 1999.
Cambridge University Press, number 9780521355643.
- Thanasis Stengos & Ximing Wu, 2010.
"Information-Theoretic Distribution Test with Application to Normality,"
Taylor & Francis Journals, vol. 29(3), pages 307-329.
- Thanasis Stengos & Ximing Wu, 2006. "Information-Theoretic Distribution Test with Application to Normality," Working Papers 0604, University of Guelph, Department of Economics and Finance.
- 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.
When requesting a correction, please mention this item's handle: RePEc:wpa:wuwpem:0510006. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (EconWPA)
If references are entirely missing, you can add them using this form.