scholarly journals Statistical Tests for the Reciprocal of a Normal Mean with a Known Coefficient of Variation

2015 ◽  
Vol 2015 ◽  
pp. 1-5
Author(s):  
Wararit Panichkitkosolkul
Keyword(s):  
Monte Carlo Simulation ◽  
Coefficient Of Variation ◽  
Statistical Tests ◽  
Taylor Series Expansion ◽  
Type I ◽  
Type I Errors ◽  
Monte Carlo Simulation Study ◽  
Asymptotic Test ◽  
Normal Mean ◽  
Simulation Results

An asymptotic test and an approximate test for the reciprocal of a normal mean with a known coefficient of variation were proposed in this paper. The asymptotic test was based on the expectation and variance of the estimator of the reciprocal of a normal mean. The approximate test used the approximate expectation and variance of the estimator by Taylor series expansion. A Monte Carlo simulation study was conducted to compare the performance of the two statistical tests. Simulation results showed that the two proposed tests performed well in terms of empirical type I errors and power. Nevertheless, the approximate test was easier to compute than the asymptotic test.

scholarly journals Approximate confidence interval for the reciprocal of a normal mean with a known coefficient of variation

2016 ◽  
Vol 13 (2) ◽  
Author(s):  
Wararit Panichkitkosolkul
Keyword(s):  
Confidence Interval ◽  
Coefficient Of Variation ◽  
Coverage Probability ◽  
Nuclear Physics ◽  
Normal Population ◽  
Taylor Series Expansion ◽  
Monte Carlo Simulation Study ◽  
Normal Mean ◽  
Simulation Results ◽  
Approximate Confidence Interval

An approximate confidence interval for the reciprocal of a normal population mean with a known coefficient of variation is proposed. This has applications in the area of nuclear physics, agriculture and economic when the researcher knows the coefficient of variation. The proposed confidence interval is based on the approximate expectation and variance of the estimator by Taylor series expansion. A Monte Carlo simulation study was conducted to compare the performance of the proposed confidence interval with the existing confidence interval. Simulation results show that the proposed confidence interval performs as well as the existing one in terms of coverage probability. However, the approximate confidence interval is very easy to calculate compared with the exact confidence interval.

scholarly journals Control charts for skewed distributions

2012 ◽  
Vol 9 (2) ◽  
Author(s):  
Derya Karagöz ◽  
Canan Hamurkaroğlu
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Control Charts ◽  
Type I ◽  
Skewed Distributions ◽  
Simulation Results ◽  
Standard Deviations ◽  
Control Limits

In this paper the control limits of \(\bar{X}\) and \(R\) control charts for skewed distributions are obtained by considering the classic, the weighted variance (\(\mathit{WV}\)), the weighted standard deviations (\(\mathit{WSD}\)) and the skewness correction (\(\mathit{SC}\)) methods. These methods are compared by using Monte Carlo simulation. Type I risk probabilities of these control charts are compared with respect to different subgroup sizes for skewed distributions which are Weibull, gamma and lognormal. Simulation results show that Type I risk of \(\mathit{SC}\) method is less than that of other methods. When the distribution is approximately symmetric, then the Type I risks of Shewhart, \(\mathit{WV}\) , \(\mathit{WSD}\), and \(\mathit{SC}\) \(\bar{X}\) charts are comparable, while the \(\mathit{SC}\) \(R\) chart has a noticeable smaller Type I risk.

scholarly journals Handling Outliers and Missing Data in Regression Models Using R: Simulation Examples

2020 ◽  
pp. 187-203
Author(s):  
Mohamed Reda Abonazel
Keyword(s):  
Monte Carlo Simulation ◽  
Missing Data ◽  
Regression Models ◽  
Missing Values ◽  
Model Simulation ◽  
Nearest Neighbors ◽  
K Nearest Neighbors ◽  
Monte Carlo Simulation Study ◽  
Simulation Results ◽  
Handling Methods

This paper has reviewed two important problems in regression analysis (outliers and missing data), as well as some handling methods for these problems. Moreover, two applications have been introduced to understand and study these methods by R-codes. Practical evidence was provided to researchers to deal with those problems in regression modeling with R. Finally, we created a Monte Carlo simulation study to compare different handling methods of missing data in the regression model. Simulation results indicate that, under our simulation factors, the k-nearest neighbors method is the best method to estimate the missing values in regression models.

Clarifying Agreement Calculations and Analysis for End-User Elicitation Studies

2022 ◽  
Vol 29 (1) ◽  
pp. 1-70
Author(s):  
Radu-Daniel Vatavu ◽  
Jacob O. Wobbrock
Keyword(s):  
Formal Model ◽  
Statistical Tests ◽  
Type I ◽  
End User ◽  
Type I Errors ◽  
Scientific Rigor ◽  
Tolerance Relation ◽  
Measures Of Agreement ◽  
Elicitation Studies ◽  
Review Current

We clarify fundamental aspects of end-user elicitation, enabling such studies to be run and analyzed with confidence, correctness, and scientific rigor. To this end, our contributions are multifold. We introduce a formal model of end-user elicitation in HCI and identify three types of agreement analysis: expert , codebook , and computer . We show that agreement is a mathematical tolerance relation generating a tolerance space over the set of elicited proposals. We review current measures of agreement and show that all can be computed from an agreement graph . In response to recent criticisms, we show that chance agreement represents an issue solely for inter-rater reliability studies and not for end-user elicitation, where it is opposed by chance disagreement . We conduct extensive simulations of 16 statistical tests for agreement rates, and report Type I errors and power. Based on our findings, we provide recommendations for practitioners and introduce a five-level hierarchy for elicitation studies.

scholarly journals Confidence Intervals for the Coefficient of Variation in a Normal Distribution with a Known Population Mean

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Wararit Panichkitkosolkul
Keyword(s):  
Confidence Interval ◽  
Normal Distribution ◽  
Confidence Intervals ◽  
Coefficient Of Variation ◽  
Coverage Probability ◽  
Normal Approximation ◽  
Monte Carlo Simulation Study ◽  
Population Mean ◽  
Expected Length ◽  
Simulation Results

This paper presents three confidence intervals for the coefficient of variation in a normal distribution with a known population mean. One of the proposed confidence intervals is based on the normal approximation. The other proposed confidence intervals are the shortest-length confidence interval and the equal-tailed confidence interval. A Monte Carlo simulation study was conducted to compare the performance of the proposed confidence intervals with the existing confidence intervals. Simulation results have shown that all three proposed confidence intervals perform well in terms of coverage probability and expected length.

scholarly journals Adsorption and diffusion of H2 and CO on UiO-66: A Monte Carlo simulation study

2020 ◽  
Vol 11 (3) ◽  
pp. 217-222
Author(s):  
Negin Davoodian ◽  
Zahra Khoshbin
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Guest Molecule ◽  
Isosteric Heat ◽  
Simulation Method ◽  
Type I ◽  
Monte Carlo Simulation Study ◽  
Guest Molecules ◽  
Metal Organic ◽  
And Diffusion

Metal-organic frameworks (MOFs) are a new class of nanoporous materials that have attracted much attention for the adsorption of small molecules, due to the large size of the cavities. In this study, we investigate the adsorption and diffusion of hydrogen (H2) and carbon monoxide (CO) guest molecules to the UiO-66 framework, as one of the most widely used MOFs, by using Monte Carlo simulation method. The results prove that an increment in the temperature decreases the amount of the adsorbed H2 and CO on the UiO-66 framework. While an enhancement of the pressure increases the amount of the adsorbed H2 and CO on the UiO-66 framework. Besides, the adsorption of H2 and CO on UiO-66 is the type I isotherm. The calculated isosteric heat for CO/UiO-66 is slightly higher than that of H2/UiO-66. The means of square displacement (MSD) value is less for CO molecule; hence, the movement of the guest molecule within the host cavity slows down and the guest molecule travels a shorter distance over a period of time. The guest molecule with higher molecular mass possesses less mobility, and therefore, it will have less permeability.

scholarly journals Statistical tests for intra-tumour clonal co-occurrence and exclusivity

2021 ◽  
Vol 17 (12) ◽  
pp. e1009036
Author(s):  
Jack Kuipers ◽  
Ariane L. Moore ◽  
Katharina Jahn ◽  
Peter Schraml ◽  
Feng Wang ◽  
...  
Keyword(s):  
Tumour Progression ◽  
Statistical Tests ◽  
Evolutionary Process ◽  
Statistical Testing ◽  
Type I ◽  
Type I Errors ◽  
Tumour Heterogeneity ◽  
Testing Framework ◽  
Single Cell Sequencing ◽  
Gene Pairs

Tumour progression is an evolutionary process in which different clones evolve over time, leading to intra-tumour heterogeneity. Interactions between clones can affect tumour evolution and hence disease progression and treatment outcome. Intra-tumoural pairs of mutations that are overrepresented in a co-occurring or clonally exclusive fashion over a cohort of patient samples may be suggestive of a synergistic effect between the different clones carrying these mutations. We therefore developed a novel statistical testing framework, called GeneAccord, to identify such gene pairs that are altered in distinct subclones of the same tumour. We analysed our framework for calibration and power. By comparing its performance to baseline methods, we demonstrate that to control type I errors, it is essential to account for the evolutionary dependencies among clones. In applying GeneAccord to the single-cell sequencing of a cohort of 123 acute myeloid leukaemia patients, we find 1 clonally co-occurring and 8 clonally exclusive gene pairs. The clonally exclusive pairs mostly involve genes of the key signalling pathways.

Underreporting in Political Science Survey Experiments: Comparing Questionnaires to Published Results

2015 ◽  
Vol 23 (2) ◽  
pp. 306-312 ◽  
Author(s):  
Annie Franco ◽  
Neil Malhotra ◽  
Gabor Simonovits
Keyword(s):  
Multiple Testing ◽  
Statistical Tests ◽  
Inductive Learning ◽  
Type I ◽  
Time Sharing ◽  
Institutional Reforms ◽  
Type I Errors ◽  
Experimental Conditions ◽  
The Social ◽  
Survey Experiments

The accuracy of published findings is compromised when researchers fail to report and adjust for multiple testing. Preregistration of studies and the requirement of preanalysis plans for publication are two proposed solutions to combat this problem. Some have raised concerns that such changes in research practice may hinder inductive learning. However, without knowing the extent of underreporting, it is difficult to assess the costs and benefits of institutional reforms. This paper examines published survey experiments conducted as part of the Time-sharing Experiments in the Social Sciences program, where the questionnaires are made publicly available, allowing us to compare planned design features against what is reported in published research. We find that: (1) 30% of papers report fewer experimental conditions in the published paper than in the questionnaire; (2) roughly 60% of papers report fewer outcome variables than what are listed in the questionnaire; and (3) about 80% of papers fail to report all experimental conditions and outcomes. These findings suggest that published statistical tests understate the probability of type I errors.

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