STATISTICAL INFERENCE ON TIME SERIES BY RKHS METHODS.

Abstract

The theory of time series is studied by probabilists (under such names as Gaussian processes and generalized processes), by statisticians (who are mainly concerned with modelling discrete parameter time series by finite parameter schemes), and by communication and control engineers (who are mainly concerned with the extraction and detection of signals in noise). The aim of this review is to outline the unifying role of reproducing kernel Hilbert spaces (RKHS) in the theory of time series. There are 13 sections (which are divided into an introduction and 4 chapters). The chapter headings are the following: Time series and RKHS; Parameter estimation and optimization; Examples of RKHS; and Probability density functionals of normal processes. (Author)

Document Details

Document Type
Technical Report
Publication Date
Jan 20, 1970
Accession Number
AD0701464

Entities

People

  • Emanuel Parzen

Organizations

  • Stanford University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Control Systems Engineering
  • Data Science
  • Detection
  • Engineers
  • Extraction
  • Gaussian Processes
  • Hilbert Space
  • Information Science
  • Interdisciplinary Science
  • Mathematical Analysis
  • Mathematics
  • Optimization
  • Probability
  • Statistical Inference

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Aerospace Engineering.
  • Theoretical Analysis.

Technology Areas

  • AI & ML
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