Statistical inference for stochastic processes with correlations
Abstract
We plan to build the ?rst demonstrably correct statistical tests for testing independence (or dependence) of pairs of paths of stoch"astic processes. As part of a broad e -ort, we will focus on tests to detect independence for the following pairs of paths of Gaussi""n processes: Wiener processes, Ornstein-Uhlenbeck (OU) processes, fractional Ornstein-Uhlenbeck (fOU), and fractional Brownian motio"n (fBm).The relevant notions of dependence which we will consider are manifold: from short-range to long-range correlation for indi"vidual paths, to whether single pairs or ?nite sets of path are statistically related, and to applied consequences as one considers"" questions of attribution of factors for real-world phenomena, particularly relating to weather and climate. Important applied goals"" include aninvestigation of climate-related risks, such as sea-level rise and extreme weather events, particularly in the North Atl""antic Ocean, how they correlate dynamically over medium and long terms, and how heavy-tailed they are. These goals could help with t"he estimation of costs of U.S. Naval vesselsand U.S. Naval installations which may be at risk from extreme weather events.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Mar 26, 2018
- Source ID
- N000141812192
Entities
People
- Philip Ernst
Organizations
- Office of Naval Research
- Rice University
- United States Navy