Applications of the Generalized Likelihood to Time Series Analysis

Abstract

Model-critical procedures provide a means to scrutinize as assumed parametric statistical model by varying the way the data are processed for repeated fits to the model. The criticism of the data is accomplished using the generalized likelihood function for the assumed probability density of the data. The degree of criticism is controlled by a user specified constant, c. The model-critical parameter estimates are obtained by maximization of the generalized likelihood function. When c=O, no criticism is performed and maximum likelihood estimates are obtained. Model-critical estimation procedures are presented for univariate and multivariate autoregressive-moving average processes. The procedures use a Kalman filter in evaluating the generalized likelihood function. A model selection criterion, based on the generalized likelihood, is also presented. A statistical test of fit for multivariate Gaussianity is presented; the test compares the model-critical and maximum likelihood estimates of the covariance matrix. Keywords: Statistical data, Tables data.

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Document Details

Document Type
Technical Report
Publication Date
Jul 28, 1988
Accession Number
ADA199101

Entities

People

  • Gerald R. Swope

Organizations

  • Naval Underwater Systems Center

Tags

Communities of Interest

  • Energy and Power Technologies
  • Ground and Sea Platforms

DTIC Thesaurus Topics

  • Computer Programs
  • Data Mining
  • Data Science
  • Estimators
  • Factorial Design
  • Goodness Of Fit Tests
  • Information Processing
  • Information Science
  • Kalman Filters
  • Mathematical Filters
  • Maximum Likelihood Estimation
  • Network Science
  • Operations Research
  • Random Variables
  • Statistical Algorithms
  • Time Series Analysis
  • Two Dimensional

Fields of Study

  • Mathematics

Readers

  • Statistical inference.