In this paper, we study a generalized Kalman--Bucy filtering problem under uncertainty. The drift uncertainty for both signal process and observation is considered, the attitude to characterized by convex operator (convex risk measure). optimal filter or minimum mean square estimator (MMSE) calculated solving estimation operator. first part of studied $g$-expectation which special For case, pro...

We obtain a limit of a hierarchical Bayes estimator of a finite population mean when the sample size is large. The limit is in the sense of ordinary calculus, where the sample observations are treated as fixed quantities. Our result suggests a simple way to correct the hierarchical Bayes estimator to achieve design-consistency, a well-known property in the traditional randomization approach to ...

Most machine learning researchers perform quantitative experiments to estimate generalization error and compare algorithm performances. In order to draw statistically convincing conclusions, it is important to estimate the uncertainty of such estimates. This paper studies the estimation of uncertainty around the K-fold cross-validation estimator. The main theorem shows that there exists no univ...

This paper presents a discrete-time robust control for electrically driven robot manipulators in the task space. A novel discrete-time model-free control law is proposed by employing an adaptive fuzzy estimator for the compensation of the uncertainty including model uncertainty, external disturbances and discretization error. Parameters of the fuzzy estimator are adapted to minimize the estimat...

In this paper we investigate the enhancement of speech by applying MMSE short-time spectral magnitude estimation in the modulation domain. For this purpose, the traditional analysis-modification-synthesis framework is extended to include modulation domain processing. We compensate the noisy modulation spectrum for additive noise distortion by applying the MMSE short-time spectral magnitude esti...

The problem addressed in this article is the localization of a mobile robot using combination onboard sensors and ultrawideband (UWB) beacons. By discrete-time formulation system's kinematics, we identify geometric conditions that make system globally observable cast state estimation into framework least-squares optimization. observability filter thus obtained remarkably different from classic ...

We consider the problem of estimating, in the presence of model uncertainties, a random vector x that is observed through a linear transformation H and corrupted by additive noise. We first assume that both the covariance of x and the transformation H are not completely specified, and develop the linear estimator that minimizes the worst-case mean-squared error (MSE) across all possible covaria...

In this paper, we propose a new multi-rate state estimator for seeking control of hard disk drives, which has a proportional, integral, and discontinuous error feedback structure to improve robustness. Conventional state estimators do not have enough accuracy under the existence of external disturbances and model uncertainty. With the proposed estimator, the seeking control system of hard disk ...