A unified approach to the design of nonlinear filters for speckle suppression in ultrasound B-mode images is presented. The detection of the (lesion) signal is formulated as a binary hypothesis-testing problem. The structure of the optimal decision rules is derived both in the case where the lesion signal is assumed either a constant or random variable. In the case of a constant signal, the max...

Consider a first order, linear and time-invariant discrete time system driven by Gaussian and zero mean white process noise, a pre-processor that accepts causal measurements of the state of the system, and a state estimator. The pre-processor and the state estimator are not co-located, and, at every timestep, the pre-processor sends either a real number or an erasure symbol to the estimator. We...

In this paper, the performance of the singleestimation (SE) and multiple-estimation (ME) is investigated in multiple-input multiple-output (MIMO) Rician flat fading channels using the traditional least squares (LS) estimator and the Bayesian minimum mean square error (MMSE) estimator. The closed form equations are obtained for mean square error (MSE) of the estimators in SE and ME cases under o...

A density ratio is defined by the ratio of two probability densities. We study the inference problem of density ratios and apply a semi-parametric density-ratio estimator to the two-sample homogeneity test. In the proposed test procedure, the f -divergence between two probability densities is estimated using a density-ratio estimator. The f -divergence estimator is then exploited for the two-sa...

A two-step generalized method of moments estimation procedure can be made robust to heteroskedasticity and autocorrelation in the data by using a nonparametric estimator of the optimal weighting matrix. This paper addresses the issue of choosing the corresponding smoothing parameter (or bandwidth) so that the resulting point estimate is optimal in a certain sense. We derive an asymptotically op...

In this paper we propose a smoothed Q-learning algorithm for estimating optimal dynamic treatment regimes. In contrast to the Q-learning algorithm in which non-regular inference is involved, we show that under assumptions adopted in this paper, the proposed smoothed Q-learning estimator is asymptotically normally distributed even when the Q-learning estimator is not and its asymptotic variance ...

In this paper, we address the distributed filtering and prediction of time-varying random fields represented by linear time-invariant (LTI) dynamical systems. The field is observed by a sparsely connected network of agents/sensors collaborating among themselves. We develop a Kalman filter type consensus+innovations distributed linear estimator of the dynamic field termed as Consensus+Innovation...

We analyze diierences between two information-theoretically motivated approaches to statistical inference and model selection: the Minimum Description Length (MDL) principle, and the Minimum Message Length (MML) principle. Based on this analysis, we present two revised versions of MML: a pointwise estimator which gives the MML-optimal single parameter model, and a volumewise estimator which giv...

A state estimator design is described for discrete time systems having observably intermittent measurements. A stationary Markov process is used to model probabilistic measurement losses. The stationarity of the Markov process suggests an analagous stationary estimator design related to the Markov states. A precomputable time-varying state estimator is proposed as an alternative to Kalman’s opt...