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A comprehensive empirical power comparison of univariate goodness-of-fit tests for the Laplace distribution

Desgagné, Alain and de Micheaux, Pierre Lafaye and Ouimet, Frédéric (2022) A comprehensive empirical power comparison of univariate goodness-of-fit tests for the Laplace distribution. Journal of Statistical Computation and Simulation . ISSN 0094-9655. doi:10.1080/00949655.2022.2082428. (In Press)

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In this paper, we present the results from an empirical power comparison of 40 goodness-of-fit tests for the univariate Laplace distribution, carried out using Monte Carlo simulations with sample sizes n = 20, 50, 100, 200, significance levels α = 0.01, 0.05, 0.10, and 400 alternatives consisting of asymmetric and symmetric light/heavy-tailed distributions taken as special cases from 11 models. In addition to the unmatched scope of our study, an interesting contribution is the proposal of an innovative design for the selection of alternatives. The 400 alternatives consist of 20 specific cases of 20 submodels drawn from the main 11 models. For each submodel, the 20 specific cases corresponded to parameter values chosen to cover the full power range. An analysis of the results leads to a recommendation of the best tests for five different groupings of the alternative distributions. A real-data example is also presented, where an appropriate test for the goodness-of-fit of the univariate Laplace distribution is applied to weekly log-returns of Amazon stock over a recent 4-year period.

Item Type:Article
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URLURL TypeDescription
Desgagné, Alain0000-0001-9047-8566
de Micheaux, Pierre Lafaye0000-0002-0247-5136
Ouimet, Frédéric0000-0001-7933-5265
Additional Information:This research includes computations performed using the computational cluster Katana supported by Research Technology Services at UNSW Sydney. Three anonymous referees have helped us greatly improve the paper. We are particularly grateful to one of the referees who spent a considerable amount of time improving its writing. We also thank the referees, the anonymous Associate Editor and the Editor in Chief for their patience.
Record Number:CaltechAUTHORS:20220912-753776600
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:116898
Deposited By: Tony Diaz
Deposited On:22 Sep 2022 19:52
Last Modified:22 Sep 2022 19:52

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