Least squares approximation to the distribution of project completion times with Gaussian uncertainty
This paper is motivated by the following question: How to construct good approximation for the distribution of the solution value to linear optimization problem when the random objective coefficients follow a multivariate normal distribution? Using Stein’s Identity, we show that the least squares no...
Saved in:
| Main Authors: | , , |
|---|---|
| 格式: | text |
| 語言: | English |
| 出版: |
Institutional Knowledge at Singapore Management University
2016
|
| 主題: | |
| 在線閱讀: | https://ink.library.smu.edu.sg/lkcsb_research/5126 https://ink.library.smu.edu.sg/context/lkcsb_research/article/6125/viewcontent/LeastSquaresApproximationDistributionProjectCompletionTimesGaussianUncertainty.pdf |
| 標簽: |
添加標簽
沒有標簽, 成為第一個標記此記錄!
|
| 機構: | Singapore Management University |
| 語言: | English |
| 總結: | This paper is motivated by the following question: How to construct good approximation for the distribution of the solution value to linear optimization problem when the random objective coefficients follow a multivariate normal distribution? Using Stein’s Identity, we show that the least squares normal approximation of the random optimal value can be computed by estimating the persistency values of the corresponding optimization problem. We further extend our method to construct a least squares quadratic estimator to improve the accuracy of the approximation; in particular, to capture the skewness of the objective. Computational studies show that the new approach provides more accurate estimates of the distributions of project completion times compared to existing methods. |
|---|