Estimation of Quantile Confidence Intervals for Queueing Systems Based on the Bootstrap Methodology

An inferential study of the phenotype for the chromosome 15q24 microdeletion syndrome: a bootstrap analysis

PeerJ ◽

10.7717/peerj.1641 ◽

2016 ◽

Vol 4 ◽

pp. e1641 ◽

Cited By ~ 2

Author(s):

Antonio Palazón-Bru ◽

Dolores Ramírez-Prado ◽

Ernesto Cortés ◽

María Soledad Aguilar-Segura ◽

Vicente Francisco Gil-Guillén

Keyword(s):

Rare Disease ◽

Confidence Intervals ◽

Literature Search ◽

Case Reports ◽

Interstitial Deletion ◽

Microdeletion Syndrome ◽

Bootstrap Analysis ◽

Wide Variability ◽

The Mean ◽

Bootstrap Methodology

In January 2012, a review of the cases of chromosome 15q24 microdeletion syndrome was published. However, this study did not include inferential statistics. The aims of the present study were to update the literature search and calculate confidence intervals for the prevalence of each phenotype using bootstrap methodology. Published case reports of patients with the syndrome that included detailed information about breakpoints and phenotype were sought and 36 were included. Deletions in megabase (Mb) pairs were determined to calculate the size of the interstitial deletion of the phenotypes studied in 2012. To determine confidence intervals for the prevalence of the phenotype and the interstitial loss, we used bootstrap methodology. Using the bootstrap percentiles method, we found wide variability in the prevalence of the different phenotypes (3–100%). The mean interstitial deletion size was 2.72 Mb (95% CI [2.35–3.10 Mb]). In comparison with our work, which expanded the literature search by 45 months, there were differences in the prevalence of 17% of the phenotypes, indicating that more studies are needed to analyze this rare disease.

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Estimators and Confidence Intervals of f2 Using Bootstrap Methodology for the Comparison of Dissolution Profiles

Computer Methods and Programs in Biomedicine ◽

10.1016/j.cmpb.2021.106449 ◽

2021 ◽

pp. 106449

Author(s):

Zhengguo Xu ◽

Matilde Merino-Sanjuan ◽

Victor Mangas-Sanjuan ◽

Alfredo Garca-Arieta

Keyword(s):

Confidence Intervals ◽

Bootstrap Methodology

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Comparing Mingoti and Glória's and Niverthi and Dey's multivariate capability indexes

Production ◽

10.1590/s0103-65132008000300014 ◽

2008 ◽

Vol 18 (3) ◽

pp. 598-608 ◽

Cited By ~ 2

Author(s):

Sueli Aparecida Mingoti ◽

Fernando Augusto Alves Glória

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Confidence Intervals ◽

Capability Index ◽

Bootstrap Methodology

In this paper a comparison between Mingoti and Glória's (2003) and Niverthi and Dey's (2000) multivariate capability indexes is presented. Monte Carlo simulation is used for the comparison and some confidence intervals were generated for the true capability index by using bootstrap methodology.

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Inferência bootstrap para intervalo de confiança de modelo de regressão quadrática

Ciência e Natura ◽

10.5902/2179460x40501 ◽

2020 ◽

Vol 42 ◽

pp. e56

Author(s):

Nicásio Gouveia ◽

Ana Lúcia Souza Silva Mateus ◽

Augusto Maciel da Silva ◽

Leandro Ferreira ◽

Suelen Carpenedo Aimi

Keyword(s):

Confidence Interval ◽

Regression Model ◽

Critical Point ◽

Confidence Intervals ◽

Regression Models ◽

Parametric Bootstrap ◽

Height Growth ◽

Point Estimate ◽

Growth Data ◽

Bootstrap Methodology

This study was carried out with the purpose of proposing a construction of confidence intervals for the critical point of a second degree regression model using a parametric bootstrap methodology. To obtain the distribution of the critical point, height growth data of the plants were used. From the analysis, the theoretical variables for the error and the confidence intervals were constructed. In addition, we examined different variance expressions with the purpose of the bootstrap-t confidence interval. The point estimate of the critical point was 10.7423 g L-1 of fertilizer doses without growth of C. canjerana plants. It was verified that the confidence intervals that considered the expression of the variance with the covariance between the regression models, present more satisfactory results, that is, results with more precision.

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