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

Abstract

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.

Leveraging Electric Network Frequency Estimation for Audio Authentication

“…The empirical data distributions seem to fit better the Laplacian distribution (i.e., solid red line) than the Gaussian distribution (i.e., blue dashed line). Table 2 summarizes the results of the Goodnessof-Fit test [53]. Let…”

“…In addition to the evidence provided in Section V-A2, a comprehensive Goodness-of-Fit test [53] is performed to assess the approximation error between x E,l and H (f c ) θ in ( 14) for the LAD-P-MLE ENF estimation scheme, which is the most accurate framework in Table 4. The test is conducted for the first 5 audio recordings from the folder H1, resulting in a total of 2873 frames.…”