Confidence interval estimators for proportions using normal approximation have been commonly used for coverage analysis of simulation output even though alternative approximate estimators of confidence intervals for proportions were proposed. This is because the normal approximation was easier to use in practice than the other approximate estimators. Computing technology has no problem with dealing these alternative estimators. Recently, one of the approximation methods for coverage analysis which is based on arcsin transformation has been used for estimating proportion and for controlling the required precision in [Raa95]. In this report, we compare three approximate interval estimators, based on a normal distribution approximation, an ...
The coefficient of variation (CV) is a helpful quantity to describe the variation in evaluating resu...
Percentiles are used everyday in descriptive statistics and data analysis. In real life, many quanti...
The oldest stochastic approximation method is the Robbins–Monro process. This estimates an unknown s...
This article is concerned with the calculation of confidence intervals for simulation output that is...
This study provides a supplemental report of the performance of the confidence interval for direct s...
Sequential analysis of simulation output is generally accepted as the most efficient way for securi...
Most of steady state simulation outputs are characterized by some degree of dependency between succe...
<p>For each simulation setting, coverage was assessed by tracking the percentage of simulations prod...
This study constructed a quadratic-based interval estimator for binomial proportion p. The modified ...
When the variance is unknown, the problem of setting fixed width confidence intervals for the mean m...
Simulated estimates for several proportions are needed in many simulation studies. We propose a meth...
This paper investigates the performance of ten methods for constructing a confidence interval estima...
This paper considers several confidence intervals for estimating the population coefficient of varia...
Reliability is one of the most important aspects of testing in educational and psychological measure...
• In applied statistics it is often necessary to obtain an interval estimate for an unknown proporti...
The coefficient of variation (CV) is a helpful quantity to describe the variation in evaluating resu...
Percentiles are used everyday in descriptive statistics and data analysis. In real life, many quanti...
The oldest stochastic approximation method is the Robbins–Monro process. This estimates an unknown s...
This article is concerned with the calculation of confidence intervals for simulation output that is...
This study provides a supplemental report of the performance of the confidence interval for direct s...
Sequential analysis of simulation output is generally accepted as the most efficient way for securi...
Most of steady state simulation outputs are characterized by some degree of dependency between succe...
<p>For each simulation setting, coverage was assessed by tracking the percentage of simulations prod...
This study constructed a quadratic-based interval estimator for binomial proportion p. The modified ...
When the variance is unknown, the problem of setting fixed width confidence intervals for the mean m...
Simulated estimates for several proportions are needed in many simulation studies. We propose a meth...
This paper investigates the performance of ten methods for constructing a confidence interval estima...
This paper considers several confidence intervals for estimating the population coefficient of varia...
Reliability is one of the most important aspects of testing in educational and psychological measure...
• In applied statistics it is often necessary to obtain an interval estimate for an unknown proporti...
The coefficient of variation (CV) is a helpful quantity to describe the variation in evaluating resu...
Percentiles are used everyday in descriptive statistics and data analysis. In real life, many quanti...
The oldest stochastic approximation method is the Robbins–Monro process. This estimates an unknown s...