We investigate the asymptotic properties of a recursive kernel density estimator associated with the driven noise of a linear regression in adaptive tracking. We provide an almost ...
We consider kernel density estimation when the observations are contaminated by measurement errors. It is well known that the success of kernel estimators depends heavily on the c...
To improve its process modeling capabilities, Los Alamos has worked toward integrating dose modeling tools with advanced discrete-event simulation tools. To date, dose information...
George Tompkins, Drew E. Kornreich, Robert Y. Park...
Quantiles, also known as value-at-risk in financial applications, are important measures of random performance. Quantile sensitivities provide information on how changes in the i...
Abstract. This paper considers the sensor network localization problem using signal strength. Unlike range-based methods signal strength information is stored in a kernel matrix. L...