Implementation of a goodness-of-fit test through Khmaladze martingale transformation

Jiwoong Kim ()
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Jiwoong Kim: Ajou University Medical Center

Computational Statistics, 2020, vol. 35, issue 4, No 20, 1993-2017

Abstract: Abstract Khmaladze martingale transformation provides an asymptotically-distribution-free method for a goodness-of-fit test. With its usage not being restricted to testing for normality, it can also be selected to test for a location-scale family of distributions such as logistic and Cauchy distributions. Despite its merits, the Khmaladze martingale transformation, however, could not have enjoyed deserved celebrity since it is computationally expensive; it entails the complex and time-consuming computations, including optimization, integration of a fractional function, matrix inversion, etc. To overcome these computational challenges, this paper proposes a fast algorithm which provides a solution to the Khmaladze martingale transformation method. To that end, the proposed algorithm is equipped with a novel strategy, named integration-in-advance, which rigorously exploits the structure of the Khmaladze martingale transformation.

Keywords: Asymptotically-distribution-free; Integration-in-advance strategy; Location-scale family; Normality test (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s00180-020-00971-7

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