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On precision of stochastic optimization based on estimates from censored data
Kybernetika, Tome 50 (2014) no. 3, pp. 297-309 Cet article a éte moissonné depuis la source Czech Digital Mathematics Library

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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.
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.
DOI : 10.14736/kyb-2014-3-0297
Classification : 62N02, 62P25, 90C15
Keywords: optimization; censored data; product-limit estimate; consistency 
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Volf, Petr. On precision of stochastic optimization based on estimates from censored data. Kybernetika, Tome 50 (2014) no. 3, pp. 297-309. doi: 10.14736/kyb-2014-3-0297

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