Name
Abstract | ||
|---|---|---|
Summary In an ordinary linear programming problem with a given set of statistical data, it is not known generally how reliable is the optimal basic solution. Our object here is to indicate a general method of reliability analysis for testing the sensitivity of the optimal basic solution and other basic solutions, in terms of expectation and variance when sample observations are available. For empirical illustration the time series data on input-output coefficients of a single farm producing three crops with three resources is used. The distributions of the first, second, and third best solutions are estimated assuming the vectors of net prices and resources to be constant and the coefficient matrix to be stochastic. Our method of statistical estimation is a combination of the Pearsonian method of moments and the maximum likelihood method. |
Year | DOI | Venue |
|---|---|---|
1965 | 10.1007/BF01976180 | Unternehmensforschung |
Field | DocType | Volume |
Time series,Mathematical optimization,Coefficient matrix,Regression analysis,Maximum likelihood,Linear programming,Basic solution,Mathematics,Method of moments (statistics) | Journal | 9 |
Issue | Citations | PageRank |
1 | 0 | 0.34 |
References | Authors | |
1 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jati K. Sengupta | 1 | 72 | 60.40 |
| Telikicherla Krishna Kumar | 2 | 0 | 0.34 |