For more information about this meeting, contact Manfred Denker.

Title: | Robust sensitivity analysis for stochastic systems |

Seminar: | Seminar on Probability and its Application |

Speaker: | Henry Lam, Boston University |

Abstract: |

Sensitivity analysis for stochastic systems is typically carried out
via derivative estimation, which critically requires parametric model
assumptions. In many situations, however, we want to evaluate model
misspecification effect beyond certain parametric family of models, or
in some cases, there plainly are no parametric model to begin with.
Motivated by such deficiency, we propose a sensitivity analysis
framework that is parameter-free, by using the Kulback-Leibler
divergence as a measure of model discrepancy, and obtain well-defined
derivative estimators. These estimators are robust in that they
automatically choose the worst (and best)-case directions to move
along in the (typically infinite-dimensional) model space. They
require little knowledge to implement; the distributions of the
underlying random variables can be known up to, for example, black-box
simulation. Methodologically, we identify these worst-case directions
of the model as changes of measure that are the fixed points of a
class of functional contraction maps. These fixed points can be
asymptotically expanded to obtain derivative estimators that are
expressed in closed-form formula in terms of the moments of certain
``symmetrizations" of the system, and hence are readily computable. |

### Room Reservation Information

Room Number: | MB106 |

Date: | 11 / 09 / 2012 |

Time: | 02:20pm - 03:20pm |