#### Filter Results:

- Full text PDF available (40)

#### Publication Year

1972

2017

- This year (2)
- Last 5 years (7)
- Last 10 years (21)

#### Publication Type

#### Co-author

#### Journals and Conferences

#### Key Phrases

Learn More

- Author Andrew Gelman, Donald B. Rubin, +13 authors Luke Tierney
- 2010

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org. Institute of… (More)

- Bruce W. Schmeiser
- Operations Research
- 1982

Batching is a commonly used method for calcuLating confidence intervals on the mean of a sequence of correlated observations arising from a simulation experiment. Several recent papers have considered the effect of using too many batches. The use of too many batches fails to satisfy assumptions of normaLity and/or independence, resulting in incorrect… (More)

- Marc S. Meketon, Bruce W. Schmeiser
- Winter Simulation Conference
- 1984

Nonoverlapping batch means (NOLBM) is a we11-known approach For estimating the variance of the sample mean. In this paper we consider an overlapping batch means (OLBM) estimator that, based on the same assumptions and batch size as NOLBM, has essentially the same mean and only 2/3 the asymptotic variance of NOLBM. Confidence interval procedures for the mean… (More)

- John S. Ramberg, Bruce W. Schmeiser
- Commun. ACM
- 1974

Tukey's lambda distribution is generalized to provide an algorithm for generating values of unimodal asymmetric random variables. This algorithm has the same advantages as the symmetric random variable generator previously given by the authors, except that the addition of another parameter complicates the problem of finding the parameter values to fit a… (More)

- Voratas Kachitvichyanukul, Bruce W. Schmeiser
- Commun. ACM
- 1988

Existing binomial random-variate generators are surveyed, and a new generator designed for moderate and large means is developed. The new algorithm, BTPE, has fixed memory requirements and is faster than other such algorithms, both when single, or when many variates are needed.

- Sherif Hashem, Bruce W. Schmeiser
- IEEE Trans. Neural Networks
- 1995

Neural network (NN) based modeling often requires trying multiple networks with different architectures and training parameters in order to achieve an acceptable model accuracy. Typically, only one of the trained networks is selected as "best" and the rest are discarded. The authors propose using optimal linear combinations (OLC's) of the corresponding… (More)

- David Goldsman, Barry L. Nelson, Bruce W. Schmeiser
- Winter Simulation Conference
- 1991

In this tutorial we consider three methods for selecting the best of a set of competings yst ems: interactive analysis, ranking and selection, and multiple comparisons. We describe each method; discuss assumptions, implement ation aspects, advantages, and disadvantages; and demonstrate the use of each method with an airline-reservation-system simulation… (More)

- John S. Ramberg, Bruce W. Schmeiser
- Commun. ACM
- 1972

A method for generating values of continuous symmetric random variables that is relatively fast, requires essentially no computer memory, and is easy to use is developed. The method, which uses a uniform zero-one random number source, is based on the inverse function of the lambda distribution of Tukey. Since it approximates many of the continuous… (More)

- Huifen Chen, Bruce W. Schmeiser
- Winter Simulation Conference
- 1994

The stochastic root-finding problem is to find the root of the equation g(z) = y, where g(z) can be estimated. There are many applications, including continuous and convex stochastic optimization, which is the problem of finding the zero of the gradient function. We propose a family of retrospective approximation algorithms that numerically solve a sequence… (More)

During the more than fifty years that Monte Carlo simulation experiments have been performed on digital computers, a wide variety of myths and common errors have evolved. We discuss some of them, with a focus on probabilistic and statistical issues.