6. SIMDAT, SIMEST, and Pseudoreality

  1. James R. Thompson

Published Online: 29 NOV 2011

DOI: 10.1002/9781118109656.ch6

Empirical Model Building: Data, Models, and Reality, Second Edition

Empirical Model Building: Data, Models, and Reality, Second Edition

How to Cite

Thompson, J. R. (2011) SIMDAT, SIMEST, and Pseudoreality, in Empirical Model Building: Data, Models, and Reality, Second Edition, John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/9781118109656.ch6

Publication History

  1. Published Online: 29 NOV 2011
  2. Published Print: 24 OCT 2011

ISBN Information

Print ISBN: 9780470467039

Online ISBN: 9781118109656

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Keywords:

  • bootstrap;
  • Dirac comb density estimator;
  • SIMDAT;
  • SIMEST;
  • stochastic model

Summary

The bootstrap is clearly a powerful algorithm for many purposes. However, given the ubiquity of fast computing, it would usually be preferred to use resampling schemes based on better nonparametric density estimators than the Dirac comb. SIMDAT is not a simple resampling so much as it is a stochastic interpolator. We can take the original data and use SIMDAT to generate a SIMDAT pseudodata set of <I>N</I> values. There are now numerous examples in several fields where SIMEST has been used to obtain estimates of the parameters characterizing a marketrelated applied stochastic process. This chapter considers an oncological application to motivate and to explicate SIMEST. It first shows a traditional model-based data analysis, and notes the serious difficulties involved. The chapter then gives a simulation-based, highly computer-intensive way to get what we require to understand the process and act on that understanding.

Controlled Vocabulary Terms

bootstrap algorithm