Improved error bounds for quantization based numerical schemes for BSDE and nonlinear filtering - Archive ouverte HAL
Article Dans Une Revue Stochastic Processes and their Applications Année : 2018

Improved error bounds for quantization based numerical schemes for BSDE and nonlinear filtering

Résumé

We take advantage of recent (see~\cite{GraLusPag1, PagWil}) and new results on optimal quantization theory to improve the quadratic optimal quantization error bounds for backward stochastic differential equations (BSDE) and nonlinear filtering problems. For both problems, a first improvement relies on a Pythagoras like Theorem for quantized conditional expectation. While allowing for some locally Lipschitz functions conditional densities in nonlinear filtering, the analysis of the error brings into playing a new robustness result about optimal quantizers, the so-called distortion mismatch property: $L^r$-quadratic optimal quantizers of size $N$ behave in $L^s$ in term of mean error at the same rate $N^{-\frac 1d}$, $0
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Dates et versions

hal-01211285 , version 1 (04-10-2015)
hal-01211285 , version 2 (24-08-2016)
hal-01211285 , version 3 (19-07-2017)

Identifiants

Citer

Gilles Pagès, Abass Sagna. Improved error bounds for quantization based numerical schemes for BSDE and nonlinear filtering. Stochastic Processes and their Applications, 2018, 128, pp.847-883. ⟨hal-01211285v3⟩
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