EFFICIENT FITTING OF LINEAR MODELS FOR CONTINUOUS STATIONARY TIME SERIES FROM DISCRETE DATA

Abstract

Consideration is given to the problem of estimating the parameters of the rational spectral density function of a continuous process given n observations equi-spaced in time. The estimates are derived by assuming the observations to be normally distributed. However, it must not be thought that the properties of the estimates depend critically on this assumption. If the dis(over) tribution is non-normal, the estimation process can be thought of as arising from a sort of generalized least-squares method. The joint distribution of the equi-spaced observations is considered, and the relevant parameters of this distribution is estimated first. These are then converted to estimates of the parameters of the spectral density. The estimation of the parameters of the underlying stochastic differentialequation model is also considered. (Author)

Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1960
Accession Number
AD0248652

Entities

People

  • James Durbin

Organizations

  • University of North Carolina at Chapel Hill

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Data Science
  • Information Science
  • Least Squares Method
  • Maximum Likelihood Estimation
  • Observation
  • Stationary

Fields of Study

  • Mathematics

Readers

  • Approximation Theory.
  • Statistical inference.

Technology Areas

  • Space