Kernel Risk-Sensitive Loss: Definition, Properties and Application to Robust Adaptive Filtering

Robust Properties of Risk-Sensitive Control

The purpose of the present paper is to precisely characterize and prove robustness properties of risk sensitive controllers. In particular, we establish a stochastic version of the small gain theorem. This theorem is expressed in terms of an inequality which bounds the average output power in terms of the input power. Since this inequality is closely related to the risk-sensitive criterion, our...

Self-organizing kernel adaptive filtering

This paper presents a model-selection strategy based on minimum description length (MDL) that keeps the kernel least-mean-square (KLMS) model tuned to the complexity of the input data. The proposed KLMS-MDL filter adapts its model order as well as its coefficients online, behaving as a self-organizing system and achieving a good compromise between system accuracy and computational complexity wi...

Robust Properties of Risk { Sensitive Control 1 Paul

Risk-Sensitive Cubature Filtering for Jump Markov Nonlinear Systems and Its Application to Land Vehicle Positioning

In this paper, a risk-sensitive filter for a class of jump Markov nonlinear systems is proposed. By using a set of weighted cubature points to approximate the intractable risk-sensitive recursions, the proposed filter could exhibit improved numerical stability with respect to the derivation of the square-root covariance, for which the unscented transform technique might halt the operations due ...

Application of nonlinear filtering to credit risk

Merton’s model views equity as a call option on the asset of the firm. Thus the asset is partially observed through the equity. Then using nonlinear filtering an explicit expression for likelihood ratio for underlying parameters in terms of the nonlinear filter is obtained. As the evolution of the filter itself depends on the parameters in question, this does not permit direct maximum likelihoo...