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Test of fit for a Laplace distribution against heavier tailed alternatives
- Source :
- Computational Statistics & Data Analysis. 54:958-965
- Publication Year :
- 2010
- Publisher :
- Elsevier BV, 2010.
-
Abstract
- Over the last decade there has been a marked interest in a Laplace distribution and its properties and generalizations, especially in the framework of financial applications. Such an interest has led to a revision and discussion of available goodness-of-fit procedures for a Laplace distribution. Indeed, since most of the studies which employ the Laplace distribution are concerned with modelling heavy tailed patterns, the modern class of possible alternatives is way broader than just testing the Laplace vs. normal distribution. In this paper we propose a new test of fit for a Laplace distribution against deviations with heavier tails than that of the reference Laplace distribution. The proposed goodness-of-fit procedure is based on sample skewness and kurtosis and a robust L"1 estimator of scale about a sample median. The developed test statistic is shown to asymptotically follow a @g^2-distribution with two degrees of freedom. Performance of the new goodness-of-fit test is illustrated by simulations and a case study.
- Subjects :
- Statistics and Probability
Laplace transform
Applied Mathematics
Laplace distribution
Variance-gamma distribution
Normal distribution
Computational Mathematics
Computational Theory and Mathematics
Goodness of fit
Heavy-tailed distribution
Skewness
Econometrics
Kurtosis
Applied mathematics
Mathematics
Subjects
Details
- ISSN :
- 01679473
- Volume :
- 54
- Database :
- OpenAIRE
- Journal :
- Computational Statistics & Data Analysis
- Accession number :
- edsair.doi...........347b7393182a121c6be8cb33141ce15f