A Nonparametric Way of Distribution Testing
Ekrem Kilic ()
Econometrics from EconWPA
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.
Keywords: distribution testing; normality; monte carlo simulation (search for similar items in EconPapers)
JEL-codes: C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm
Note: Type of Document - pdf; pages: 22
References: View references in EconPapers View complete reference list from CitEc
Citations Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: http://EconPapers.repec.org/RePEc:wpa:wuwpem:0510006
Access Statistics for this paper
More papers in Econometrics from EconWPA
Series data maintained by EconWPA ().