Construction of fixed width confidence intervals for a Bernoulli success probability using sequential sampling: a simulation study

2011 ◽  
Vol 81 (11) ◽  
pp. 1483-1493 ◽  
Author(s):  
Wei Liu ◽  
Sanyu Zhou
Keyword(s):  
Confidence Intervals ◽  
Simulation Study ◽  
Sequential Sampling ◽  
Success Probability

scholarly journals Test-Retest Reliability is not a Measure of Reliability or Stability: A Friendly Reminder

2020 ◽  
Author(s):  
Lukas Röseler ◽  
Daniel Wolf ◽  
Johannes Leder ◽  
Astrid Schütz
Keyword(s):  
Confidence Intervals ◽  
Simulation Study ◽  
Repeated Measurement ◽  
Reliability Coefficient ◽  
Diagnostic Instrument ◽  
Retest Reliability ◽  
Cronbach's Alpha ◽  
Correction For Attenuation ◽  
The Stability ◽  
Test Retest Reliability

We argue that the test-retest reliability coefficient, which is the correlation between a measurement and a repeated measurement using the same diagnostic instrument in the same sample (sometimes referred to as repeatability or falsely referred to as stability), is by itself not an appropriate measure of the reliability of the diagnostic instrument or of the stability of the construct in question. In combination with an actual coefficient of reliability such as Cronbach’s alpha, the test-retest reliability coefficient can be used to estimate and compare the stabilities of constructs using a procedure based on the correction for attenuation. However, results from a simulation study showed that classically constructed confidence intervals for the estimator exhibit under-coverage and thus cannot be interpreted correctly.

scholarly journals Overlap coefficients based on Kullback-Leibler divergence: Exponential populations case

2017 ◽  
Vol 6 (4) ◽  
pp. 135
Author(s):  
Hamza Dhaker ◽  
Papa Ngom ◽  
Malick Mbodj
Keyword(s):  
Statistical Inference ◽  
Mean Square Error ◽  
Confidence Intervals ◽  
Taylor Series ◽  
Simulation Study ◽  
Invariance Property ◽  
Mean Square ◽  
Taylor Series Approximation ◽  
Leibler Divergence ◽  
Exponential Populations

This article is devoted to the study of overlap measures of densities of two exponential populations. Various Overlapping Coefficients, namely: Matusita’s measure ρ, Morisita’s measure λ and Weitzman’s measure ∆. A new overlap measure Λ based on Kullback-Leibler measure is proposed. The invariance property and a method of statistical inference of these coefficients also are presented. Taylor series approximation are used to construct confidence intervals for the overlap measures. The bias and mean square error properties of the estimators are studied through a simulation study.

Confidence intervals in ridge regression by bootstrapping the dependent variable: a simulation study

1995 ◽  
Vol 24 (3) ◽  
pp. 631-652 ◽  
Author(s):  
Ana Crivelli ◽  
Luis Firinguetti ◽  
Rosa Montaño ◽  
Margarita Muñóz
Keyword(s):  
Confidence Intervals ◽  
Simulation Study ◽  
Ridge Regression

Exact confidence limits for prevalence of a disease with an imperfect diagnostic test

2010 ◽  
Vol 138 (11) ◽  
pp. 1674-1678 ◽  
Author(s):  
J. REICZIGEL ◽  
J. FÖLDI ◽  
L. ÓZSVÁRI
Keyword(s):  
Confidence Intervals ◽  
Diagnostic Test ◽  
Sensitivity And Specificity ◽  
Simulation Study ◽  
Confidence Level ◽  
Asymptotic Methods ◽  
Real Life ◽  
Disease Status ◽  
Confidence Limits ◽  
Extensive Simulation

SUMMARYEstimation of prevalence of disease, including construction of confidence intervals, is essential in surveys for screening as well as in monitoring disease status. In most analyses of survey data it is implicitly assumed that the diagnostic test has a sensitivity and specificity of 100%. However, this assumption is invalid in most cases. Furthermore, asymptotic methods using the normal distribution as an approximation of the true sampling distribution may not preserve the desired nominal confidence level. Here we proposed exact two-sided confidence intervals for the prevalence of disease, taking into account sensitivity and specificity of the diagnostic test. We illustrated the advantage of the methods with results of an extensive simulation study and real-life examples.

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