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December 1, 1972Journal of the American Statistical Association241 citations

Linear Dynamic Recursive Estimation from the Viewpoint of Regression Analysis

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DDDavid B. DuncanSHSusan D. Horn

Key Points

  • To connect engineering-based recursive time series methods with statistical regression theory through an extended random-β regression framework.
  • Formulated random-β regression theory as a direct mathematical extension of standard fixed-β linear regression.
  • Derived optimal recursive estimators using the extended regression framework for standard multivariate dynamic models.
  • Demonstrated that multivariate recursive time series models commonly used in engineering map directly onto extended regression frameworks.
  • Established analytical derivations of optimal recursive estimators within the extended regression paradigm to facilitate further statistical developments.

Abstract

Abstract A large class of useful multivariate recursive time series models and estimation methods has appeared in the engineering literature. Despite the interest and utility which this recursive work has when viewed as an extension of regression analysis, little of it has reached statisticians working in regression. To overcome this we (a) present the relevant random-β regression theory as a natural extension of conventional fixed-β regression theory and (b) derive the optimal recursive estimators in terms of the extended regression theory for a typical form of the recursive model. This also opens the way for further developments in recursive estimation, which are more tractable in the regression approach and will be presented in future papers.

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Cite This Study

Duncan et al. (1972) studied this question.

synapsesocial.com/papers/6a0f35175f469783126ca7c2https://doi.org/10.1080/01621459.1972.10481299
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