scholarly journals Reliability Estimation of Inverse Lomax Distribution Using Extreme Ranked Set Sampling

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Amer Ibrahim Al-Omari ◽  
Amal S. Hassan ◽  
Naif Alotaibi ◽  
Mansour Shrahili ◽  
Heba F. Nagy
Keyword(s):  
Ranked Set Sampling ◽  
Reliability Estimation ◽  
Simple Random Sample ◽  
Stress Distributions ◽  
Reliability Estimate ◽  
Lomax Distribution ◽  
Set Size ◽  
Reliability Estimates ◽  
Inverse Lomax Distribution ◽  
Extreme Ranked Set Sampling

In survival analysis, the two-parameter inverse Lomax distribution is an important lifetime distribution. In this study, the estimation of R = P   Y < X is investigated when the stress and strength random variables are independent inverse Lomax distribution. Using the maximum likelihood approach, we obtain the R estimator via simple random sample (SRS), ranked set sampling (RSS), and extreme ranked set sampling (ERSS) methods. Four different estimators are developed under the ERSS framework. Two estimators are obtained when both strength and stress populations have the same set size. The two other estimators are obtained when both strength and stress distributions have dissimilar set sizes. Through a simulation experiment, the suggested estimates are compared to the corresponding under SRS. Also, the reliability estimates via ERSS method are compared to those under RSS scheme. It is found that the reliability estimate based on RSS and ERSS schemes is more efficient than the equivalent using SRS based on the same number of measured units. The reliability estimates based on RSS scheme are more appropriate than the others in most situations. For small even set size, the reliability estimate via ERSS scheme is more efficient than those under RSS and SRS. However, in a few cases, reliability estimates via ERSS method are more accurate than using RSS and SRS schemes.

TEST PLAN ALLOCATION TO MINIMIZE SYSTEM RELIABILITY ESTIMATION VARIABILITY

2004 ◽  
Vol 11 (03) ◽  
pp. 257-272 ◽  
Author(s):  
JOSE E. RAMIREZ-MARQUEZ ◽  
DAVID W. COIT ◽  
TONGDAN JIN
Keyword(s):  
System Reliability ◽  
Reliability Estimation ◽  
System Structure ◽  
System Level ◽  
Reliability Estimate ◽  
Component Level ◽  
Component Reliability ◽  
Allocation Process ◽  
Reliability Estimates ◽  
Estimate Variance

A new methodology is presented to allocate testing units to the different components within a system when the system configuration is fixed and there are budgetary constraints limiting the amount of testing. The objective is to allocate additional testing units so that the variance of the system reliability estimate, at the conclusion of testing, will be minimized. Testing at the component-level decreases the variance of the component reliability estimate, which then decreases the system reliability estimate variance. The difficulty is to decide which components to test given the system-level implications of component reliability estimation. The results are enlightening because the components that most directly affect the system reliability estimation variance are often not those components with the highest initial uncertainty. The approach presented here can be applied to any system structure that can be decomposed into a series-parallel or parallel-series system with independent component reliability estimates. It is demonstrated using a series-parallel system as an example. The planned testing is to be allocated and conducted iteratively in distinct sequential testing runs so that the component and system reliability estimates improve as the overall testing progresses. For each run, a nonlinear programming problem must be solved based on the results of all previous runs. The testing allocation process is demonstrated on two examples.

scholarly journals A New Estimation Study of the Stress-Strength Reliability for the Topp–Leone Distribution Using Advanced Sampling Methods

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Abdullah M. Almarashi ◽  
Ali Algarni ◽  
Amal S. Hassan ◽  
M. Elgarhy ◽  
Farrukh Jamal ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Random Variables ◽  
Simple Random Sampling ◽  
Ranked Set Sampling ◽  
Independent Random Variables ◽  
Simple Random Sample ◽  
Simulation Research ◽  
Set Size ◽  
Median Ranked Set Sampling ◽  
Advanced Sampling

In this manuscript, we investigate the estimation of the unknown reliability measure R = P [Y < X], in the case where Y and X are two independent random variables with Topp–Leone distributions. As the main contribution, various advanced sampling strategies are studied. The suggested strategies are simple random, ranked set, and median ranked set samplings. Firstly, based on the maximum likelihood, we give an efficient estimator of R when the observations of the two random variables are selected from the same simple random sample. Secondly, such an estimator is addressed when the observations of the two random variables are selected from the ranked set sampling method. Then, based on median ranked set sampling, the maximum likelihood estimator of R is addressed in all the four cases. When the observations from the two random variables are selected from the same set size, two cases are considered, while the other two cases are considered at different set sizes. A simulation research is developed to evaluate the behavior of the obtained estimates based on standard and median ranked set samplings with their simple random sampling equivalents. The ratio of mean square error is used to assess the effectiveness of these estimates.

scholarly journals Further Increasing Fisher's Information for Parameters of Association in Accelerated Failure Time Models via Double Extreme Ranks

2019 ◽  
pp. 679-700
Author(s):  
Hani Samawi ◽  
Amal Helu ◽  
Herash Rochani
Keyword(s):  
Failure Time ◽  
Auxiliary Variable ◽  
Ranked Set Sampling ◽  
Survival Models ◽  
Simple Random Sample ◽  
Accelerated Failure Time ◽  
Simulation Studies ◽  
Accelerated Failure Time Models ◽  
Accelerated Failure ◽  
Extreme Ranked Set Sampling

Double Extreme Ranked Set Sampling (DERSS) was first introduced by Samawi (2002) as a modification to the well-known Ranked Set Sampling (RSS) and Extreme Ranked Set Sampling (ERSS). In this article, we provide a modification to DERSS scheme with ranking based on an easy-to-rank baseline auxiliary variable known to be associated with survival time. We show that using the modified DERSS improves the performance of the Accelerated failure time (AFT) survival model and provides a more efficient estimator of the hazard ratio than that based on their counter parts simple random sample (SRS), RSS and ERSS, which results in reducing the sample size required and hence the total cost of the study. Our theoretical and simulation studies show the superiority of using the modified DERSS for AFT survival models compared with using SRS, RSS and ERSS.  A numerical example based on Worcester Heart Attack Study is presented to illustrate the implementation of the DERSS.

A Comparison of Reliability Estimation Based on Confirmatory Factor Analysis and Exploratory Structural Equation Models

2021 ◽  
pp. 001316442110089
Author(s):  
Yuanshu Fu ◽  
Zhonglin Wen ◽  
Yang Wang
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Structural Equation ◽  
Reliability Estimation ◽  
Model Fit ◽  
Equation Modeling ◽  
Factor Loadings ◽  
Reliability Estimates ◽  
Confirmatory Factor ◽  
Composite Reliability

Composite reliability, or coefficient omega, can be estimated using structural equation modeling. Composite reliability is usually estimated under the basic independent clusters model of confirmatory factor analysis (ICM-CFA). However, due to the existence of cross-loadings, the model fit of the exploratory structural equation model (ESEM) is often found to be substantially better than that of ICM-CFA. The present study first illustrated the method used to estimate composite reliability under ESEM and then compared the difference between ESEM and ICM-CFA in terms of composite reliability estimation under various indicators per factor, target factor loadings, cross-loadings, and sample sizes. The results showed no apparent difference in using ESEM or ICM-CFA for estimating composite reliability, and the rotation type did not affect the composite reliability estimates generated by ESEM. An empirical example was given as further proof of the results of the simulation studies. Based on the present study, we suggest that if the model fit of ESEM (regardless of the utilized rotation criteria) is acceptable but that of ICM-CFA is not, the composite reliability estimates based on the above two models should be similar. If the target factor loadings are relatively small, researchers should increase the number of indicators per factor or increase the sample size.

Reliability Estimates for IRT-Based Forced-Choice Assessment Scores

2021 ◽  
pp. 109442812199908
Author(s):  
Yin Lin
Keyword(s):  
Impression Management ◽  
Empirical Studies ◽  
Forced Choice ◽  
Reliability Estimation ◽  
Estimation Methods ◽  
High Stakes ◽  
Personnel Decisions ◽  
Assessment Scores ◽  
Reliability Estimates ◽  
Different Types

Forced-choice (FC) assessments of noncognitive psychological constructs (e.g., personality, behavioral tendencies) are popular in high-stakes organizational testing scenarios (e.g., informing hiring decisions) due to their enhanced resistance against response distortions (e.g., faking good, impression management). The measurement precisions of FC assessment scores used to inform personnel decisions are of paramount importance in practice. Different types of reliability estimates are reported for FC assessment scores in current publications, while consensus on best practices appears to be lacking. In order to provide understanding and structure around the reporting of FC reliability, this study systematically examined different types of reliability estimation methods for Thurstonian IRT-based FC assessment scores: their theoretical differences were discussed, and their numerical differences were illustrated through a series of simulations and empirical studies. In doing so, this study provides a practical guide for appraising different reliability estimation methods for IRT-based FC assessment scores.

The Fear of COVID-19 Scale: A Reliability Generalization Meta-Analysis

Assessment ◽  
2021 ◽  
pp. 107319112199416
Author(s):  
Desirée Blázquez-Rincón ◽  
Juan I. Durán ◽  
Juan Botella
Keyword(s):  
Predictive Model ◽  
Meta Analysis ◽  
Classical Test Theory ◽  
Measurement Model ◽  
Rasch Measurement ◽  
Test Theory ◽  
Reliability Estimate ◽  
Reliability Generalization ◽  
Reliability Estimates ◽  
Total Variability

A reliability generalization meta-analysis was carried out to estimate the average reliability of the seven-item, 5-point Likert-type Fear of COVID-19 Scale (FCV-19S), one of the most widespread scales developed around the COVID-19 pandemic. Different reliability coefficients from classical test theory and the Rasch Measurement Model were meta-analyzed, heterogeneity among the most reported reliability estimates was examined by searching for moderators, and a predictive model to estimate the expected reliability was proposed. At least one reliability estimate was available for a total of 44 independent samples out of 42 studies, being that Cronbach’s alpha was most frequently reported. The coefficients exhibited pooled estimates ranging from .85 to .90. The moderator analyses led to a predictive model in which the standard deviation of scores explained 36.7% of the total variability among alpha coefficients. The FCV-19S has been shown to be consistently reliable regardless of the moderator variables examined.

A Test-Retest Reliability Generalization Meta-Analysis of Judgments Via the Policy-Capturing Technique

2021 ◽  
pp. 109442812110115
Author(s):  
Ze Zhu ◽  
Alan J. Tomassetti ◽  
Reeshad S. Dalal ◽  
Shannon W. Schrader ◽  
Kevin Loo ◽  
...  
Keyword(s):  
Best Practice ◽  
Meta Analysis ◽  
Temporal Stability ◽  
Future Research ◽  
Policy Capturing ◽  
Reliability Estimate ◽  
Reliability Generalization ◽  
Retest Reliability ◽  
Reliability Estimates ◽  
Test Retest Reliability

Policy capturing is a widely used technique, but the temporal stability of policy-capturing judgments has long been a cause for concern. This article emphasizes the importance of reporting reliability, and in particular test-retest reliability, estimates in policy-capturing studies. We found that only 164 of 955 policy-capturing studies (i.e., 17.17%) reported a test-retest reliability estimate. We then conducted a reliability generalization meta-analysis on policy-capturing studies that did report test-retest reliability estimates—and we obtained an average reliability estimate of .78. We additionally examined 16 potential methodological and substantive antecedents to test-retest reliability (equivalent to moderators in validity generalization studies). We found that test-retest reliability was robust to variation in 14 of the 16 factors examined but that reliability was higher in paper-and-pencil studies than in web-based studies and was higher for behavioral intention judgments than for other (e.g., attitudinal and perceptual) judgments. We provide an agenda for future research. Finally, we provide several best-practice recommendations for researchers (and journal reviewers) with regard to (a) reporting test-retest reliability, (b) designing policy-capturing studies for appropriate reportage, and (c) properly interpreting test-retest reliability in policy-capturing studies.

scholarly journals To BAN or Not to BAN: Bayesian Attention Networks for Reliable Hate Speech Detection

2021 ◽  
Author(s):  
Kristian Miok ◽  
Blaž Škrlj ◽  
Daniela Zaharie ◽  
Marko Robnik-Šikonja
Keyword(s):  
Monte Carlo ◽  
Hate Speech ◽  
Classification Performance ◽  
Reliability Estimation ◽  
Superior Performance ◽  
Speech Detection ◽  
Attention Networks ◽  
Reliability Estimates ◽  
Viable Mechanism ◽  
Affective Dimensions

AbstractHate speech is an important problem in the management of user-generated content. To remove offensive content or ban misbehaving users, content moderators need reliable hate speech detectors. Recently, deep neural networks based on the transformer architecture, such as the (multilingual) BERT model, have achieved superior performance in many natural language classification tasks, including hate speech detection. So far, these methods have not been able to quantify their output in terms of reliability. We propose a Bayesian method using Monte Carlo dropout within the attention layers of the transformer models to provide well-calibrated reliability estimates. We evaluate and visualize the results of the proposed approach on hate speech detection problems in several languages. Additionally, we test whether affective dimensions can enhance the information extracted by the BERT model in hate speech classification. Our experiments show that Monte Carlo dropout provides a viable mechanism for reliability estimation in transformer networks. Used within the BERT model, it offers state-of-the-art classification performance and can detect less trusted predictions.

On Two Novel Applications of Ranked Set Sampling

2003 ◽  
Vol 54 (1-2) ◽  
pp. 105-114
Author(s):  
Sukuman Sarikavanij ◽  
Montip Tiensuw
Keyword(s):  
Case Studies ◽  
Random Sample ◽  
Ranked Set Sampling ◽  
Simple Random Sample ◽  
Single Family ◽  
Ranked Set Sample ◽  
Population Mean ◽  
Sales Data ◽  
Tree Data ◽  
Novel Applications

In this paper we discuss two case studies which clearly indicate the advantages of using a ranked set sample (RSS) over those of a simple random sample (SRS). The applications of RSS considered here cover single family homes sales data, and tree data. It is demonstrated that in each case RSS is much more efficient than SRS for estimation of population mean.

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