Approximation of CDF of Non-Central Chi-Square Distribution by Mean-Value Theorems for Integrals

The cumulative distribution function of the non-central chi-square distribution <inline-formula><math display="inline"><semantics><mrow><msubsup><mi>χ</mi><mi>n</mi><mrow><mo>′</mo><mn>2</mn></mrow>&l...

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Bibliographic Details
Main Authors: Árpád Baricz, Dragana Jankov Maširević, Tibor K. Pogány
Format: Article
Language:English
Published: MDPI AG 2021-01-01
Series:Mathematics
Subjects:
Online Access:https://www.mdpi.com/2227-7390/9/2/129
Description
Summary:The cumulative distribution function of the non-central chi-square distribution <inline-formula><math display="inline"><semantics><mrow><msubsup><mi>χ</mi><mi>n</mi><mrow><mo>′</mo><mn>2</mn></mrow></msubsup><mrow><mo>(</mo><mi>λ</mi><mo>)</mo></mrow></mrow></semantics></math></inline-formula> of <i>n</i> degrees of freedom possesses an integral representation. Here we rewrite this integral in terms of a lower incomplete gamma function applying two of the second mean-value theorems for definite integrals, which are of Bonnet type and Okamura’s variant of the du Bois–Reymond theorem. Related results are exposed concerning the small argument cases in cumulative distribution function (CDF) and their asymptotic behavior near the origin.
ISSN:2227-7390