8 accepted papers
This paper addresses the challenge of composite binary hypothesis testing in the presence of outliers. Within this framework, we introduce a new robust score-type detector. The proposed detector, called K-score test (K-ST), relies on an empirical version of the K-divergence that utilizes Parzen’s no…
This paper presents a new framework for robust regression analysis. Under this framework, the input-output relation of a system is inferred by minimizing a new robust loss which relates the outputs to a presumed parametric function of the inputs. The considered loss arises from a modified version of…
In this paper, we develop an expectation-maximization (EM)-like scheme, called ${\mathcal{K}}$-BM, for iterative numerical computation of the minimum ${\mathcal{K}}$-divergence estimator (M${\mathcal{K}}$DE). This estimator utilizes Parzen’s non-parameteric ${\mathcal{K}}$ernel density estimate to d…
In this paper we present a new divergence, called $\mathcal{K}$-divergence, that involves a weighted version of the hypothesized log-likelihood function. To down-weight low density areas, attributed to outliers, the corresponding weight function is a convolved version of the underlying density with…
In this paper, we develop a new robust spectrum sensing method for MIMO cognitive radios in the presence of heavy-tailed noise. The proposed sensing technique, called measure-transformed covariance test (MTCT), operates by applying a transform to the probability measure of the data. The considered p…
The measure-transformed (MT) MUltiple SIgnal Classification (MUSIC) algorithm is a robust MUSIC generalization that operates by applying a transform to the probability measure (distribution) of the data. In this paper, we first provide an asymptotic mean-squared-error (MSE) performance analysis of t…
In this paper, a generalization of the Gaussian quasi likelihood ratio test (GQLRT) for simple hypotheses is developed. The proposed generalization, called measure-transformed GQLRT (MT-GQLRT), selects a Gaussian probability model that best empirically fits a transformed probability measure of the d…
In this paper, we consider the problem of estimating a deterministic vector parameter when the likelihood function is unknown or not expressible. We develop an estimator, called measure-transformed quasi maximum likelihood estimator (MT-QMLE), that minimizes the empirical Kullback-Leibler divergence…