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14 pages, 434 KB

Open AccessArticle

Energy Statistic-Based Goodness-of-Fit Test for the Lindley Distribution with Application to Lifetime Data

by Joseph Njuki and Ryan Avallone

Stats 2025, 8(4), 87; https://doi.org/10.3390/stats8040087 - 26 Sep 2025

Cited by 1 | Viewed by 765

In this article, we propose a goodness-of-fit test for a one-parameter Lindley distribution based on energy statistics. The Lindley distribution has been widely used in reliability studies and survival analysis, especially in applied sciences. The proposed test procedure is simple and more powerful against general alternatives. Under different settings, Monte Carlo simulations show that the proposed test is able to be well controlled for any given nominal levels. In terms of power, the proposed test outperforms other existing similar methods in different settings. We then apply the proposed test to real-life datasets to demonstrate its competitiveness and usefulness. Full article

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24 pages, 328 KB

Open AccessArticle

Improved EDF-Based Tests for Weibull Distribution Using Ranked Set Sampling

by Safar M. Alghamdi, Rashad A. R. Bantan, Amal S. Hassan, Heba F. Nagy, Ibrahim Elbatal and Mohammed Elgarhy

Mathematics 2022, 10(24), 4700; https://doi.org/10.3390/math10244700 - 11 Dec 2022

Cited by 7 | Viewed by 1878

Abstract

It is well known that ranked set sampling (RSS) is superior to conventional simple random sampling (SRS) in that it frequently results in more effective inference techniques. One of the most popular and broadly applicable models for lifetime data is the Weibull distribution. This article proposes different modified goodness-of-fit tests based on the empirical distribution function (EDF) for the Weibull distribution. The recommended RSS tests are compared to their SRS counterparts. For each scheme, the critical values of the relevant test statistics are computed. A comparison of the power of the suggested goodness-of-fit tests based on a number of alternatives is investigated. RSS tests are more effective than their SRS equivalents, according to simulated data. Full article

(This article belongs to the Section D1: Probability and Statistics)

21 pages, 19938 KB

Open AccessArticle

A Multiscale Denoising Framework Using Detection Theory with Application to Images from CMOS/CCD Sensors

by Khuram Naveed, Shoaib Ehsan, Klaus D. McDonald-Maier and Naveed Ur Rehman

Sensors 2019, 19(1), 206; https://doi.org/10.3390/s19010206 - 8 Jan 2019

Cited by 18 | Viewed by 6417

Abstract

Output from imaging sensors based on CMOS and CCD devices is prone to noise due to inherent electronic fluctuations and low photon count. The resulting noise in the acquired image could be effectively modelled as signal-dependent Poisson noise or as a mixture of Poisson and Gaussian noise. To that end, we propose a generalized framework based on detection theory and hypothesis testing coupled with the variance stability transformation (VST) for Poisson or Poisson–Gaussian denoising. VST transforms signal-dependent Poisson noise to a signal independent Gaussian noise with stable variance. Subsequently, multiscale transforms are employed on the noisy image to segregate signal and noise into separate coefficients. That facilitates the application of local binary hypothesis testing on multiple scales using empirical distribution function (EDF) for the purpose of detection and removal of noise. We demonstrate the effectiveness of the proposed framework with different multiscale transforms and on a wide variety of input datasets. Full article

(This article belongs to the Section Physical Sensors)

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