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A modified Kolmogorov-Smirnov test for the inverse gaussian density with unknown parameters

 

作者: Rick L. Edgeman,   Robert C. Scott,   Robert J. Pavur,  

 

期刊: Communications in Statistics - Simulation and Computation  （Taylor Available online 1988）
卷期: Volume 17, issue 4  

页码: 1203-1212

 

ISSN:0361-0918

 

年代: 1988

 

DOI:10.1080/03610918808812721

 

出版商: Marcel Dekker, Inc.

 

关键词: empirical distribution function;goodness‐of‐fit;Monte Carlo simulation

 

数据来源: Taylor

 

摘要:

The Kolmogorov-Smirnov (KS) test is an empirical distribution function (EDF) based goodness-of-fit test that requires the underlying hypothesized density to be continuous and completely specified. When the parameters are unknown and must be estimated from the data, standard tables of the KS test statistic are not valid. Approximate upper tail percentage points of the KS statistic for the inverse Gaussian (IG) distribution with unknown parameters are tabled in this paper.

 

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