Evaluating best-case and worst-case variances when bounds are available
This paper describes procedures for computing the tightest possible best-case and worst-case bounds on the variance of a discrete, bounded, random variable when lower and upper bounds are available for its unknown probability mass function. An example from the application of the Monte Carlo method t...
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| Veröffentlicht in: | SIAM journal on scientific and statistical computing 1992-11, Vol.13 (6), p.1347-1360 |
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| container_end_page | 1360 |
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| container_issue | 6 |
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| container_title | SIAM journal on scientific and statistical computing |
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| creator | FISHMAN, G. S GRANOVSKY, B. L RUBIN, D. S |
| description | This paper describes procedures for computing the tightest possible best-case and worst-case bounds on the variance of a discrete, bounded, random variable when lower and upper bounds are available for its unknown probability mass function. An example from the application of the Monte Carlo method to the estimation of network reliability illustrates the procedures and, in particular, reveals considerable tightening in the worst-case bound when compared to the trivial worst-case bound based exclusively on range. |
| doi_str_mv | 10.1137/0913076 |
| format | Article |
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| identifier | ISSN: 0196-5204 |
| ispartof | SIAM journal on scientific and statistical computing, 1992-11, Vol.13 (6), p.1347-1360 |
| issn | 0196-5204 1064-8275 2168-3417 1095-7197 |
| language | eng |
| recordid | cdi_proquest_journals_921663978 |
| source | SIAM Journals Online |
| subjects | Algorithms Exact sciences and technology Lagrange multiplier Linear programming Mathematical foundations Mathematics Probability and statistics Random variables Sciences and techniques of general use Statistics |
| title | Evaluating best-case and worst-case variances when bounds are available |
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