Differential Geometrical Methods in Time Series.

Abstract

Inference for time series processes was investigated using differential geometrical methods as well as sampling based Bayesian methods. For univariate autoregressive moving average (ARMA) processes and fractionally integrated ARMA processes analytical forms of asymptotic properties of inference such as bias in parameter estimates and improved test statistics were obtained from geometrical quantities. These terms provide collections useful when the sample size is moderate or small. Markov chain Monte Carlo procedures facilitated modeling of univariate and multivariate ARMA and fractionally integrated ARMA processes in the Bayesian framework. This approach uses the exact likelihood function and is accurate even with small sample sizes. Outlier analysis, prediction and model selection were addressed. (AN)

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1995
Accession Number
ADA304006

Entities

People

  • Nalini Ravishanker

Organizations

  • University of Connecticut

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Bayesian Inference
  • Bayesian Networks
  • Computational Science
  • Curvature
  • Data Science
  • Differential Geometry
  • Geometry
  • Information Science
  • Knowledge Management
  • Markov Chains
  • Models
  • Monte Carlo Method
  • Sampling
  • Statistical Algorithms
  • Statistics

Fields of Study

  • Mathematics

Readers

  • Approximation Theory.
  • Computational Modeling and Simulation

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms