A measure of location is examined that places itself where the signed rank deviations are as close to zero as possible. A solution algorithm is sketched. The measure is robust to outliers. In three illustrative real data examples we find the measure is usually intermediate between mean and...

The Federal Communications Commission (FCC) has requested comments on a proposal to end the regulation of Internet Service Providers ISPs) as information companies under Section 706 of the Communications Act of 1934 and regulate ISPs as common carrier telecommunications companies under the more...

Network-Centric Meaning-Driven Human-Centric AI-Cyber Computing Beyond Data-Driven to Event-Driven Architectures for Quantum Uncertainty, 1995-2023:Building upon the contextual focus of current global worldwide discussions on GPT, ChatGPT, GenAI, Generative AI, Large Language Model - LLMs, we...

Let ηt be a Poisson point process of intensity t≥1 on some state space Y and let f be a non-negative symmetric function on Yk for some k≥1. Applying f to all k-tuples of distinct points of ηt generates a point process ξt on the positive real half-axis. The scaling limit of ξt as t tends...

We establish a simple variance inequality for U-statistics whose underlying sequence of random variables is an ergodic Markov Chain. The constants in this inequality are explicit and depend on computable bounds on the mixing rate of the Markov Chain. We apply this result to derive the strong law...

In this paper we present a test for the maximal rank of the matrix-valued volatility process in the continuous Ito semimartingale framework. Our idea is based upon a random perturbation of the original high frequency observations of an Ito semimartingale, which opens the way for rank testing. We...

The bootstrap is a popular and useful tool for estimating the asymptotic variance of complicated estimators. Ironically, the fact that the estimators are complicated can make the standard bootstrap computationally burdensome because it requires repeated re-calculation of the estimator. In...

The bootstrap is a popular and useful tool for estimating the asymptotic variance of complicated estimators. Ironically, the fact that the estimators are complicated can make the standard bootstrap computationally burdensome because it requires repeated re-calculation of the estimator. In...