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Computing the Kolmogorov-Smirnov Distribution when the Underlying cdf is Purely Discrete, Mixed or Continuous

Dimitrova, D. S., Kaishev, V. K. and Tan, S. (2017). Computing the Kolmogorov-Smirnov Distribution when the Underlying cdf is Purely Discrete, Mixed or Continuous. .

Abstract

The distribution of the Kolmogorov-Smirnov (K-S) test statistic has been widely studied under the assumption that the underlying theoretical cdf, F(x), is continuous. However, there are many real-life applications in which fitting discrete or mixed distributions is required. Nevertheless, due to inherent difficulties, the distribution of the K-S statistic when F(x) has jump discontinuities has been studied to a much lesser extent and no exact and efficient computational methods have been proposed in the literature. In this paper, we provide a fast and accurate method to compute the (complementary) cdf of the K-S statistic when F(x) is discontinuous, and thus obtain exact p values of the K-S test. Our approach is to express the complementary cdf through the rectangle probability for uniform order statistics, and to compute it using Fast Fourier Transform(FFT). Secondly, we provide a C++ and an R implementation of the proposed method, which fills in the existing gap in statistical software. We give also a useful extension of the Schmid’s asymptotic formula for the distribution of the K-S statistic, relaxing his requirement for F(x) to be increasing between jumps and thus allowing for any general mixed or purely discrete F(x). The numerical performance of the proposed FFT-based method, implemented both in C++ and in the R package KSgeneral, is illustrated when F(x) is mixed, purely discrete, and continuous. The performance of the general asymptotic formula is also studied.

Publication Type: Monograph (Working Paper)
Publisher Keywords: Kolmogorov-Smirnov test statistic; discontinuous (discrete or mixed) distribution; Fast Fourier Transform; double boundary non-crossing; rectangle probability for uniform order statistics
Departments: Cass Business School > Actuarial Science & Insurance
URI: http://openaccess.city.ac.uk/id/eprint/18541
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