Sample entropy (SampEn) is a nonlinear regularity index that requires the a priori selection of three parameters: the length of the sequences to be compared, m, the patterns similarity tolerance, r, and the number of samples under analysis, N. Appropriate values for m, r and N have been recommended and widely used in the literature for the application of SampEn to some physiological time series...

In this article we perform an asymptotic analysis of Bayesian parallel kernel density estimators introduced by Neiswanger, Wang and Xing [19]. We derive the asymptotic expansion of the mean integrated squared error for the full data posterior estimator and investigate the properties of asymptotically optimal bandwidth parameters. Our analysis demonstrates that partitioning data into subsets req...

Volume 20, Number 1 12 Dr. William C. Roberts, editor in chief, announced the cowinners of the sixth Ralph R. Tompsett Writing Award. Bertrand J. Brown, MD, who was a fellow in transplant anesthesiology until July 2006, published the article, “Use of dexmedetomidine versus general anesthesia for endovascular repair of abdominal aortic aneurysms,” in the July 2006 issue. Coauthors of the article...

Kernel density estimation (KDE) is a popular nonparametric density estimation method. We (1) derive finite-sample high-probability density estimation bounds for multivariate KDE under mild density assumptions which hold uniformly in x ∈ R and bandwidth matrices. We apply these results to (2) mode, (3) density level set, and (4) class probability estimation and attain optimal rates up to logarit...

Title Type identification and stochastic adaptive control systems & control foundations & applications PDF digital control systems volume 2 stochastic control multivariable control adaptive control applications PDF stochastic systems estimation identification and adaptive control prentice-hall information and system sciences series PDF model-based control of networked systems systems & control ...

In this paper, we consider admissible estimation of the parameter ?r in the gamma distribution with truncated parameter space under entropy loss function. We obtain the classes of admissible estimators. The result can be applied to estimation of parameters in the normal, lognormal, pareto, generalized gamma, generalized Laplace and other distributions.