Test of fit for a Laplace distribution against heavier tailed alternatives

Gel, Yulia R.

doi:10.1016/j.csda.2009.10.008

Cited by 13 publications

(13 citation statements)

References 23 publications

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“…Both return processes (the upper and the lower graphs in Figure 3) display the pattern of stationarity, clustered volatilities and mean close to zero. The P values of the Jarque–Bera test (Jarque & Bera, 1980), the Robust Jarque–Bera test (Gel & Gastwirth, 2008) and the SJ test (Gel, Miao, & Gastwirth, 2007) for both log‐returns are all shown to be less than 10 −16 , indicating that the log return processes for Euro/USD and DAX are heavier than normal, which confirms the previous findings of Ullrich (2009) on currency exchange rates and discussion of DAX log‐returns by Küchler et al (1999), Claessen & Mittnik (2002), Haas, Mittnik, & Paolella (2006), and Gel (2010). Hence, the new robust permutation and bootstrap tests might be the preferred procedures.…”

Section: Case Studies On Analysis Of the Euro/us Dollar Exchange Rasupporting

confidence: 82%

Robust Lagrange multiplier test for detecting ARCH/GARCH effect using permutation and bootstrap

Gel

Chen

2012

Can J Statistics

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The Lagrange Multiplier (LM) test is one of the principal tools to detect ARCH and GARCH effects in financial data analysis. However, when the underlying data are non‐normal, which is often the case in practice, the asymptotic LM test, based on the χ2‐approximation of critical values, is known to perform poorly, particularly for small and moderate sample sizes. In this paper we propose to employ two re‐sampling techniques to find critical values of the LM test, namely permutation and bootstrap. We derive the properties of exactness and asymptotically correctness for the permutation and bootstrap LM tests, respectively. Our numerical studies indicate that the proposed re‐sampled algorithms significantly improve size and power of the LM test in both skewed and heavy‐tailed processes. We also illustrate our new approaches with an application to the analysis of the Euro/USD currency exchange rates and the German stock index. The Canadian Journal of Statistics 40: 405–426; 2012 © 2012 Statistical Society of Canada

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Section: Case Studies On Analysis Of the Euro/us Dollar Exchange Rasupporting

confidence: 82%

Robust Lagrange multiplier test for detecting ARCH/GARCH effect using permutation and bootstrap

Gel

Chen

2012

Can J Statistics

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“…Such a comparison includes 22 tests available in the literature with 27 possible distributions (symmetric or asymmetric) presented as alternatives to the Laplace distribution. To our best knowledge this study is the first attempt made in the literature to compare so many goodness-of-fit tests for the Laplace distribution and alternative distributions (c.f., Best, Rayner, and Thas (2008) and Gel (2010)). Section 5 presents a summary of the results of the paper and some practical relevant conclusions.…”

Section: Introductionmentioning

confidence: 98%

“…Yen and Moore 1988;Rublík 1997;Puig and Stephens 2000a;Chen 2002, and references cited therein); (ii) tests based on the empirical characteristic function (c.f. Meintanis 2005); (iii) moment based tests, i.e., tests based on sample moments, skewness and kurtosis (González-Estrada and Villaseñor 2016;Gel 2010;Rayner and Best 1989;Langholz and Kronmal 1991;Li and Papadopoulos 2002); (iv) entropy and divergence based tests (Choi and Kim 2006; (Gulati 2011).…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Goodness-of-Fit Tests for the Laplace Distribution

Batsidis¹,

Economou²,

Bar‐Lev³

2022

AJS

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The Laplace distribution is one of the earliest distributions in probability theory and is a frequently used distribution in many fields. Consequently, various goodness-of-fit tests for the Laplace distribution have been thoroughly derived in theliterature. The purpose of this paper is to carry out a comparative study of these tests as well as a new one we develop. Power comparisons of all such tests are performed via Monte Carlo simulations of sample data generatedfrom twenty seven alternatives distributions. Despite the fact that no single test was found to be most powerful in all situations, several useful recommendations however are made.

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“…Iliopoulos and Balakrishnan [12] developed exact inference for the location and scale parameters of Laplace distribution based on their maximum likelihood estimators (MLEs) with a Type-II censored data. Gel [13] proposed a new test of fit for a Laplace distribution based on sample skewness and kurtosis and a robust L1estimator of scale about a sample median. Shi et al [14] studied robust estimation for linear and mixture linear errorsin-variables regression models based on the relationship between the least absolute deviation criterion and MLE in a Laplace distribution.…”

Section: Introductionmentioning

confidence: 99%

Bayes Estimation of Parameter of Laplace Distribution Under a New LINEX-Based Loss Function

Li¹

2017

IJDSA

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Loss function is one of the most topics in Bayesian analysis. The aim of this paper is to study the estimation of the shape parameter of Laplace distribution using Bayesian technique under a new loss function, which is a compound function of LINEX function. The Bayes estimator of the parameter is derived under the prior distribution of the parameter based on Gamma prior distribution. Furthermore, Monte Carlo statistical simulations illustrate that the Bayes estimators obtained under LINEX-based loss function is affected by the prior parameter and the value of the shape parameter of the LINEX-based loss function. But when the sample size is large, they have less influence on the estimation result.

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Test of fit for a Laplace distribution against heavier tailed alternatives

Cited by 13 publications

References 23 publications

Robust Lagrange multiplier test for detecting ARCH/GARCH effect using permutation and bootstrap

Robust Lagrange multiplier test for detecting ARCH/GARCH effect using permutation and bootstrap

A Comparative Study of Goodness-of-Fit Tests for the Laplace Distribution

Bayes Estimation of Parameter of Laplace Distribution Under a New LINEX-Based Loss Function

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