Kybernetika 50 no. 3, 297-309, 2014

On precision of stochastic optimization based on estimates from censored data

Petr VolfDOI: 10.14736/kyb-2014-3-0297

Abstract:

In the framework of a stochastic optimization problem, it is assumed that the stochastic characteristics of optimized system are estimated from randomly right-censored data. Such a case is frequently encountered in time-to-event or lifetime studies. The analysis of precision of such a solution is based on corresponding theoretical properties of estimated stochastic characteristics. The main concern is to show consistency of optimal solution even in the random censoring case. Behavior of solutions for finite data sizes is studied with the aid of randomly generated example.

Keywords:

consistency, optimization, censored data, product-limit estimate

Classification:

62N02, 62P25

paper.pdf

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