Reference interval estimation of small sample sizes: A methodologic comparison using a computer‐simulation study

2019 ◽  
Vol 48 (2) ◽  
pp. 335-346 ◽  
Author(s):  
Kevin Le Boedec
Keyword(s):  
Computer Simulation ◽  
Simulation Study ◽  
Interval Estimation ◽  
Reference Interval ◽  
Small Sample ◽  
Sample Sizes ◽  
Computer Simulation Study ◽  
Small Sample Sizes

scholarly journals RECOMBINATION AND ASSORTMENT IN THE MACRONUCLEUS OF TETRAHYMENA THERMOPHILA: A THEORETICAL STUDY BY COMPUTER SIMULATION

Genetics ◽  
1984 ◽  
Vol 108 (4) ◽  
pp. 1035-1045
Author(s):  
F P Doerder ◽  
S L Diblasi
Keyword(s):  
Cell Cycle ◽  
Computer Simulation ◽  
Theoretical Study ◽  
Tetrahymena Thermophila ◽  
Recombination Frequency ◽  
Small Sample ◽  
Sample Sizes ◽  
Cell Cycles ◽  
Macronuclear Development ◽  
Small Sample Sizes

ABSTRACT The compound nature of the macronucleus of Tetrahymena thermophila presents multiple opportunities for recombination between genes on the same macronuclear chromosome. Such recombinants should be detectable through their assortment at subsequent amitotic macronuclear divisions. Thus, a macronucleus that is initially AB/ab should produce recombinant assortees of the genotypes Ab/aB. Computer simulation shows that, when the recombination frequency is two or fewer times per cell cycle, recombinant assortees are produced at experimentally measurable frequencies of less than 40%. At higher recombination frequencies, linked genes appear to assort independently. The simulations also show that recombination during macronuclear development can be distinguished from recombination in subsequent cell cycles only if the first appearance of recombinant assortees is 100 or more fissions after conjugation. The use of macronuclear recombination and assortment as a means of mapping macronuclear genes is severely constrained by the large variances in assortment outcomes; with experimentally small sample sizes, such mapping is impossible.

scholarly journals Confidence Regions for Multivariate Quantiles

Water ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 996 ◽  
Author(s):  
Maximilian Coblenz ◽  
Rainer Dyckerhoff ◽  
Oliver Grothe
Keyword(s):  
Simulation Study ◽  
Level Sets ◽  
Confidence Regions ◽  
Small Sample ◽  
Sample Sizes ◽  
Coverage Probabilities ◽  
Sample Application ◽  
Small Sample Sizes ◽  
Reliable Methods ◽  
Insight Into

Multivariate quantiles are of increasing importance in applications of hydrology. This calls for reliable methods to evaluate the precision of the estimated quantile sets. Therefore, we focus on two recently developed approaches to estimate confidence regions for level sets and extend them to provide confidence regions for multivariate quantiles based on copulas. In a simulation study, we check coverage probabilities of the employed approaches. In particular, we focus on small sample sizes. One approach shows reasonable coverage probabilities and the second one obtains mixed results. Not only the bounded copula domain but also the additional estimation of the quantile level pose some problems. A small sample application gives further insight into the employed techniques.

scholarly journals Analysis of power of the classical and robust normality tests against bimodal distribution

2009 ◽  
Vol 57 (6) ◽  
pp. 253-260
Author(s):  
Luboš Střelec
Keyword(s):  
Simulation Study ◽  
Bimodal Distribution ◽  
Small Sample ◽  
The Other ◽  
Sample Sizes ◽  
Test Statistic ◽  
Lilliefors Test ◽  
The Mean ◽  
Anderson Darling ◽  
Small Sample Sizes

The aim of this paper is to compare the power of selected normality tests to detect a bimodal distribution. We use some classical normality tests (the Shapiro-Wilk test, the Lilliefors test, the Anderson-Darling test, the classical Jarque-Bera test and the Jarque-Bera-Urzua test), some robust normality tests (the robust Jarque-Bera test and the Medcouple test) and the modified Jarque-Bera tests, where the median instead of the mean is used in the classical Jarque-Bera test statistic. The results of simulation study show that the Anderson-Darling and the Shapiro-Wilk tests outperform the others, especially in small sample sizes. On the other hand the classical Jarque-Bera, the Jarque-Bera-Urzua and robust Jarque-Bera tests are biased, especially in small sample sizes again. Finally, the modification of the Jarque-Bera test leads to increase of power against bimodal distribution.

scholarly journals Sample Size Requirements for Calibrated Approximate Credible Intervals for Proportions in Clinical Trials

2021 ◽  
Vol 18 (2) ◽  
pp. 595
Author(s):  
Fulvio De Santis ◽  
Stefania Gubbiotti
Keyword(s):  
Clinical Trials ◽  
Posterior Probability ◽  
Interval Estimation ◽  
Small Sample ◽  
Sample Sizes ◽  
Posterior Density ◽  
Unknown Parameters ◽  
Credible Intervals ◽  
Small Sample Sizes ◽  
Highest Posterior Density

In Bayesian analysis of clinical trials data, credible intervals are widely used for inference on unknown parameters of interest, such as treatment effects or differences in treatments effects. Highest Posterior Density (HPD) sets are often used because they guarantee the shortest length. In most of standard problems, closed-form expressions for exact HPD intervals do not exist, but they are available for intervals based on the normal approximation of the posterior distribution. For small sample sizes, approximate intervals may be not calibrated in terms of posterior probability, but for increasing sample sizes their posterior probability tends to the correct credible level and they become closer and closer to exact sets. The article proposes a predictive analysis to select appropriate sample sizes needed to have approximate intervals calibrated at a pre-specified level. Examples are given for interval estimation of proportions and log-odds.

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