Evaluation of Bootstrap Confidence Intervals Using a New Non-Normal Process Capability Index

Rao, G. Srinivasa; Albassam, Mohammed; Aslam, Muhammad

doi:10.3390/sym11040484

Cited by 11 publications

(11 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[63]. The maximum likelihood estimation with robust standard errors was utilized in structural equation and path analysis to adjust for the use of continuous variables with non-normal distributions [64,65]. Model comparisons were determined using the loglikelihood ratio test (LRT; See Appendix A for formula) as recommended by Muthén and Muthén [66].…”

Section: Primary Analysismentioning

confidence: 99%

Examining the Role of Anxiety and Depression in Dietary Choices among College Students

et al. 2020

View full text Add to dashboard Cite

This study examines the role of anxiety and depression symptoms in predicting dietary choices in emerging adults while accounting for sex differences in these relationships. Participants were 225 English speaking undergraduates enrolled in a university in southeastern United States. Participants were recruited through an online research recruitment application utilized by the university. Participants volunteered for a two-phased anonymous survey monitoring the effects of eating habits and gastrointestinal health in young adults. As part of this effort, participants completed self-reporting measures related to anxiety and depression, as well as an automated, self-administered 24-h diet recall. Multigroup path analysis was used to test primary hypotheses. Overall, a decrease in total caloric intake and an increase in sugar consumption were found as self-reported symptoms of anxiety and depression increased. In addition, there were sex differences in the relationship between depression and food choices. Men consumed more saturated fat as well as less fruits and vegetables as self-reported symptoms of depression increased. Results suggest symptoms of depression are a greater risk factor for poor nutrition in male college students than females. The findings provide another justification to screen for psychological distress in student health services given the implications on behavioral lifestyle and health.

show abstract

Section: Primary Analysismentioning

confidence: 99%

Examining the Role of Anxiety and Depression in Dietary Choices among College Students

et al. 2020

View full text Add to dashboard Cite

show abstract

“…It should be noted that the CI proposed by Luceño 18 has an error in asymptotic theory of statistics and we, therefore, provide a corrected version of the same CI. Second, we obtain the nonparametric BCIs using both the BCI approaches by Franklin and Wasserman 30 and the modified BCI approaches by Park et al 28 For other references on the BCI approaches, the readers are referred to the references, [33][34][35][36][37][38][39] among others. Third, we compare the powers of the two approaches using the relation between CIs and hypothesis testing.…”

Section: Introductionmentioning

confidence: 99%

“…Second, we obtain the nonparametric BCIs using both the BCI approaches by Franklin and Wasserman 30 and the modified BCI approaches by Park et al 28 . For other references on the BCI approaches, the readers are referred to the references, 33–39 among others. Third, we compare the powers of the two approaches using the relation between CIs and hypothesis testing.…”

Section: Introductionmentioning

confidence: 99%

Confidence intervals of the process capability index Cpc$C_{pc}$ revisited via modified bootstrap technique and ROC curves

Ouyang

Dey²,

Byun

et al. 2023

Quality & Reliability Eng

View full text Add to dashboard Cite

We consider the process capability index (PCI), a widely used quality‐related statistic used to assess the quality of products and performance of monitored processes in various industries. It is widely known that the conventional PCIs perform well when the quality process being monitored has a normal distribution. Unfortunately, using the indices to evaluate a non‐normally distributed process often leads to inaccurate results. In this article, we consider a new PCI, , that can be used in both normal and non‐normal scenarios. The objective of this article is threefold: (i) We provide a corrected form of the confidence interval for . (ii) We compare the performance of three nonparametric bootstrap confidence intervals (BCIs) for . Specifically, the standard bootstrap, percentile bootstrap, and bias‐corrected percentile bootstrap. Under various distributional assumptions such as the normal, chi‐square, Student t, Laplace, and two‐parameter exponential distributions, the estimated coverage probabilities and average width of the confidence intervals and BCIs for are compared. (iii) The power of the respective bootstrap approaches is evaluated by using the equivalence relation between confidence interval construction and two‐sided hypothesis testing. We also provide the receiver operating characteristic curves to evaluate their performance. Finally, as an illustrative example, an actual data set related to groove dimensions (in inches) measured from a manufacturing process of ignition keys is re‐analyzed to illustrate the utility of the proposed methods.

show abstract

“…Recently, great additions have been found in literature regarding bootstrap CIs and their behaviors for processes that violate the normality assumption. The readers may refer to Rao et al, 21,22 Peng, 23,24 Dey and Saha, [25][26][27] Saha et al, 28,29 Pina-Monarrez et al, 30 Leiva et al, 31 Pearn et al, 32 Weber et al, 33 and Kashif et al 34,35 The extensive use of PCIs in manufacturing processes necessitates the detailed study of proposed PCI structures in the literature using different estimation methods and modified distributions. Generally, the Bayesian and classical/frequentist are two estimation categories classified in contemporary statistics, and almost all statistical analyses are based on these two classifications.…”

Section: Introductionmentioning

confidence: 99%

Bootstrap confidence intervals of process capability indices Spmk, Spmkc and Cs for Frechet distribution

Kanwal

Abbas

2023

Quality & Reliability Eng

View full text Add to dashboard Cite

In this paper, we considered the Bayesian estimators under reference and Jeffery's priors and maximum likelihood estimators to estimate the unknown parameters of the process capability indices Spmk, Spmkc, and Cs for Frechet distribution. Further, we developed bootstrap confidence intervals for aforementioned process capability indices based on above‐mentioned estimators. Monte Carlo simulations are performed to investigate the performance of process capability indices through skewness, kurtosis, mean square error and widths of bootstrap confidence intervals for small, moderate, and large sample sizes. Simulations results indicate that the Bayesian estimator under reference prior outperforms even in small sample sizes, and all performed equally well for larger sample sizes. Moreover, the average width for bootstrap confidence interval for Cs is least in all. Finally, real data is analyzed for illustration purposes.

show abstract

Evaluation of Bootstrap Confidence Intervals Using a New Non-Normal Process Capability Index

Cited by 11 publications

References 14 publications

Examining the Role of Anxiety and Depression in Dietary Choices among College Students

Examining the Role of Anxiety and Depression in Dietary Choices among College Students

Confidence intervals of the process capability index Cpc$C_{pc}$ revisited via modified bootstrap technique and ROC curves

Bootstrap confidence intervals of process capability indices Spmk, Spmkc and Cs for Frechet distribution

Contact Info

Product

Resources

About