# Shrinkage strategies in some multiple multi-factor dynamical systems

ESAIM: Probability and Statistics (2012)

- Volume: 16, page 139-150
- ISSN: 1292-8100

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topNkurunziza, Sévérien. "Shrinkage strategies in some multiple multi-factor dynamical systems." ESAIM: Probability and Statistics 16 (2012): 139-150. <http://eudml.org/doc/276391>.

@article{Nkurunziza2012,

abstract = {In this paper, we are interested in estimation problem for the drift parameters matrices of m independent multivariate diffusion processes. More specifically, we consider the case where the m-parameters matrices are supposed to satisfy some uncertain constraints. Given such an uncertainty, we develop shrinkage estimators which improve over the performance of the maximum likelihood estimator (MLE). Under an asymptotic distributional quadratic risk criterion, we study the relative dominance of the established estimators. Further, we carry out simulation studies for observation periods of small and moderate lengths of time that corroborate the theoretical finding for which shrinkage estimators outperform over the MLE. The proposed method is useful in model assessment and variable selection. },

author = {Nkurunziza, Sévérien},

journal = {ESAIM: Probability and Statistics},

keywords = {Asymptotic distributional risk; diffusion process; MLE; Shrinkage estimator; Wiener process.; asymptotic distributional risk; shrinkage estimator; Wiener process},

language = {eng},

month = {7},

pages = {139-150},

publisher = {EDP Sciences},

title = {Shrinkage strategies in some multiple multi-factor dynamical systems},

url = {http://eudml.org/doc/276391},

volume = {16},

year = {2012},

}

TY - JOUR

AU - Nkurunziza, Sévérien

TI - Shrinkage strategies in some multiple multi-factor dynamical systems

JO - ESAIM: Probability and Statistics

DA - 2012/7//

PB - EDP Sciences

VL - 16

SP - 139

EP - 150

AB - In this paper, we are interested in estimation problem for the drift parameters matrices of m independent multivariate diffusion processes. More specifically, we consider the case where the m-parameters matrices are supposed to satisfy some uncertain constraints. Given such an uncertainty, we develop shrinkage estimators which improve over the performance of the maximum likelihood estimator (MLE). Under an asymptotic distributional quadratic risk criterion, we study the relative dominance of the established estimators. Further, we carry out simulation studies for observation periods of small and moderate lengths of time that corroborate the theoretical finding for which shrinkage estimators outperform over the MLE. The proposed method is useful in model assessment and variable selection.

LA - eng

KW - Asymptotic distributional risk; diffusion process; MLE; Shrinkage estimator; Wiener process.; asymptotic distributional risk; shrinkage estimator; Wiener process

UR - http://eudml.org/doc/276391

ER -

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