Performances of some goodness-of-fit tests for sampling designs in ranked set sampling

Yildiz, Tuğba Özkal; Sevil, Yusuf Can

doi:10.1080/00949655.2018.1444765

Cited by 6 publications

(5 citation statements)

References 14 publications

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“…As previously noted, it is not uncommon to find simulations in the literature where negative h parameters are used (e.g., He & Raghunathan, 2006; Vallejo et al., 2010; Teh, Othman, & Khoo, 2010; Yildiz & Sevil, 2018; Zhang & Algina, 2011). Such simulations invariably focus on controlling the first four moments of the distributions to simulate distributions with different skew and excess kurtosis.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…In order to ensure unimodality, among other desirable properties of the distribution ( e.g ., unbounded support and non‐singular probability density function (pdf)), it is further required that h >0 (McDonald & Turley, 2011). Nevertheless, published articles have used values outside of these bounds in cases where kurtosis was being used as a simulation condition ( e.g ., Branco, Oliveira, & Oliveira, 2010; Luh & Guo, 1999; Ozdemir, Wilcox, & Yildiztepe 2013) and even negative values of the h parameter are quite common ( e.g ., He & Raghunathan, 2006; Vallejo, Ato, & Fernandez, 2010; Teh, Othman, & Khoo 2010; Yildiz & Sevil, 2018; Zhang & Algina, 2011). Analytical control of the moments of the distribution is only possible for h >0 (McDonald & Turley, 2011); as such, in this section the h parameter is restricted to being positive for ease of exposition.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

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Theoretical considerations when simulating data from the g‐and‐h family of distributions

Astivia

Kroc

2022

Brit J Math & Statis

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The g‐and‐h family of distributions is a computationally efficient, flexible option to model and simulate non‐normal data. In spite of its popularity, there are several theoretical aspects of these distributions that need special consideration when they are used. In this paper some of these aspects are explored. In particular, through mathematical analysis it is shown that a popular multivariate generalization of the g‐and‐h distribution may result in marginal distributions which are no longer g‐and‐h distributed, that more than one set of (g,h) parameters can correspond to the same values of population skewness and excess kurtosis, and that multivariate generalizations of g‐and‐h distributions available in the literature are special cases of Gaussian copula distributions. A small‐scale simulation is also used to demonstrate how simulation conclusions can change when different (g,h) parameters are used to simulate data, even if they imply the same population values of skewness and excess kurtosis.

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Section: Theoretical Backgroundmentioning

confidence: 99%

Section: Theoretical Backgroundmentioning

confidence: 99%

Theoretical considerations when simulating data from the g‐and‐h family of distributions

Astivia

Kroc

2022

Brit J Math & Statis

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“…Recently, statistical inference for mean, total, variance and quantiles have been discussed by Ozturk [25,26,27], Ozturk and Bayramoglu Kavlak [28,29] in finite population setting. Also, Sevil and Yildiz [30,31] and Yildiz and Sevil [32,33] proposed the following empirical distribution function (EDF) in finite population setting.…”

Section: Introductionmentioning

confidence: 99%

“…If the distribution function of the sheep weights is estimated, both the quantiles and the probabilities corresponding to the specific quantiles can be obtained. Unlike the studies Sevil and Yildiz [30,31] and Yildiz and Sevil [32,33], we provide design-based estimators for distribution function. These estimators use the information of inclusion probabilities as well.…”

Section: Introductionmentioning

confidence: 99%

Design-based estimators of distribution function in ranked set sampling with an application

Sevil,

Yildiz

2022

Preprint

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Empirical distribution functions (EDFs) based on ranked set sampling (RSS) and its modifications have been examined by many authors. In these studies, the proposed estimators have been investigated for infinite population setting. However, developing EDF estimators in finite population setting would be more valuable for areas such as environmental, ecological, agricultural, biological, etc. This paper introduces new EDF estimators based on level-0, level-1 and level-2 sampling designs in RSS. Asymptotic properties of the new EDF estimators have been established. Numerical results have been obtained for the case when ranking is imperfect under different distribution functions. It has been observed that level-2 sampling design provides more efficient EDF estimator than its counterparts of level-0, level-1 and simple random sampling. In real data application, we consider a pointwise estimate of distribution function and estimation of the median of sheep's weights at seven months using RSS based on level-2 sampling design.

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“…These tests include the Kolmogorov-Smirnov (KS) and Cramer-Von Mises (CVM) GOF tests discussed in [12] who gave a practical guide to GOF tests using statistics based on EDF. A comprehensive survey of GOF tests based on SRS can be found in [13], while when using RSS samples, these tests can be obtained simply by replacing the SRS EDF with the unbiased RSS EDF see [14]. GOF indices such as AIC, CAIC, HQIC, and BIC are used for model selection and provide fair comparisons between different distribution candidates.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Performance of Some Ranked Set Sampling Designs Using HybridApproach

Sabry¹,

Almetwally²,

Almongy³

et al. 2021

Computers, Materials &Amp; Continua

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In this paper, a joint analysis consisting of goodness-of-fit tests and Markov chain Monte Carlo simulations are used to assess the performance of some ranked set sampling designs. The Markov chain Monte Carlo simulations are conducted when Bayesian methods with Jeffery's priors of the unknown parameters of Weibull distribution are used, while the goodness of fit analysis is conducted when the likelihood estimators are used and the corresponding empirical distributions are obtained. The ranked set sampling designs considered in this research are the usual ranked set sampling, extreme ranked set sampling, median ranked set sampling, and neoteric ranked set sampling designs. An intensive Monte Carlo simulation study is conducted using Lindley's approximation algorithm to compute the different designs'based estimators. The study showed that the dependent design "neoteric ranked set sampling design" is superior to other ranked set designs and the total relative efficiency is higher than the other designs' total relative efficiency.

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Performances of some goodness-of-fit tests for sampling designs in ranked set sampling

Cited by 6 publications

References 14 publications

Theoretical considerations when simulating data from the g‐and‐h family of distributions

Theoretical considerations when simulating data from the g‐and‐h family of distributions

Design-based estimators of distribution function in ranked set sampling with an application

Assessing the Performance of Some Ranked Set Sampling Designs Using HybridApproach

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