Journal ArticleDOI
Simulation Run Length Control in the Presence of an Initial Transient
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TLDR
A procedure based on Schruben's Brownian bridge model for the detection of nonstationarity and a spectral method for estimating the variance of the sample mean are explored for estimation of the steady state mean of an output sequence from a discrete event simulation.Abstract:
This paper studies the estimation of the steady state mean of an output sequence from a discrete event simulation. It considers the problem of the automatic generation of a confidence interval of prespecified width when there is an initial transient present. It explores a procedure based on Schruben's Brownian bridge model for the detection of nonstationarity and a spectral method for estimating the variance of the sample mean. The procedure is evaluated empirically for a variety of output sequences. The performance measures considered are bias, confidence interval coverage, mean confidence interval width, mean run length, and mean amount of deleted data. If the output sequence contains a strong transient, then inclusion of a test for stationarity in the run length control procedure results in point estimates with lower bias, narrower confidence intervals, and shorter run lengths than when no check for stationarity is performed. If the output sequence contains no initial transient, then the performance measures of the procedure with a stationarity test are only slightly degraded from those of the procedure without such a test. If the run length is short relative to the extent of the initial transient, the stationarity tests may not be powerful enough to detect the transient, resulting in a procedure with unreliable point and interval estimates.read more
Citations
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Journal ArticleDOI
Convergence of Probability Measures
TL;DR: Convergence of Probability Measures as mentioned in this paper is a well-known convergence of probability measures. But it does not consider the relationship between probability measures and the probability distribution of probabilities.
Journal ArticleDOI
On Bayesian Analysis of Mixtures with an Unknown Number of Components (with discussion)
TL;DR: In this paper, a hierarchical prior model is proposed to deal with weak prior information while avoiding the mathematical pitfalls of using improper priors in the mixture context, which can be used as a basis for a thorough presentation of many aspects of the posterior distribution.
Journal ArticleDOI
Markov Chain Monte Carlo Convergence Diagnostics: A Comparative Review
TL;DR: All of the methods in this work can fail to detect the sorts of convergence failure that they were designed to identify, so a combination of strategies aimed at evaluating and accelerating MCMC sampler convergence are recommended.
Journal ArticleDOI
A hierarchical regression approach to meta-analysis of diagnostic test accuracy evaluations
TL;DR: A hierarchical regression model for meta-analysis of studies reporting estimates of test sensitivity and specificity is described, which allows more between- and within-study variability than fixed-effect approaches, by allowing both test stringency and test accuracy to vary across studies.
On Bayesian Analysis of Mixtures with an Unknown Number of Components
TL;DR: In this article, a hierarchical prior model is used to deal with weak prior information while avoiding the mathematical pitfalls of using improper priors in the mixture context, and a sample from the full joint distribution of all unknown variables is generated, and this can be used as a basis for a thorough presentation of many aspects of the posterior distribution.
References
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Book
Convergence of Probability Measures
TL;DR: Weak Convergence in Metric Spaces as discussed by the authors is one of the most common modes of convergence in metric spaces, and it can be seen as a form of weak convergence in metric space.
Journal ArticleDOI
Convergence of Probability Measures
TL;DR: Convergence of Probability Measures as mentioned in this paper is a well-known convergence of probability measures. But it does not consider the relationship between probability measures and the probability distribution of probabilities.
Book
Time Series: Data Analysis and Theory
TL;DR: This book will be most useful to applied mathematicians, communication engineers, signal processors, statisticians, and time series researchers, both applied and theoretical.
Book
The Statistical Analysis of Time Series
TL;DR: The Wiley Classics Library as discussed by the authors is a collection of books that have become recognized classics in their respective fields, including some of the most important works of the 20th century in mathematics.
Book
The statistical analysis of series of events
David Cox,Peter A W Lewis +1 more
TL;DR: This monograph is intended as a survey of some of the problems in theoretical statistics that stem from this sort of data, and has tried to give a simple description, with numerical examples, of the main methods that have been proposed.