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An Extension of the Gauss-Markov Theorem for Mixed Linear Regression Models with Non-Stationary Stochastic Parameters

  • Estela Bee Dagum
  • Pierre A. Cholette

Abstract

The presence of fixed and stochastic parameters in a mixed linear regression model has been dealt with for the case where the stochastic parameters follow a stationary process. The solution is given either by Generalized Least Squares (e.g. Rao, 1965, p. 192) or by a recursive state space estimation procedure such as the Kalman filter and smoother (e.g. Sallas and Harville 1981). The estimation of non-stationary stochastic parameters has been mainly approached in the state space framework, either as an initial condition problem (see among others, Ansley and Kohn, 1985, 1989; Kohn and Ansley, 1986; Bell and Hillmer, 1991 and De Jong, 1989, 1991); or as a hierarchical model with “fixed” effects in the hierarchy given a flat prior distribution (see Sallas and Harville, 1981, 1988; Tsimikas and Ledolter, 1994).

Keywords

Kalman Filter Time Series Analysis American Statistical Association ARIMA Model Generalize Little Square 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Physica-Verlag Heidelberg 1999

Authors and Affiliations

  • Estela Bee Dagum
    • 1
  • Pierre A. Cholette
    • 2
  1. 1.Statistical SciencesUniversity of BolognaBolognaItaly
  2. 2.Time Series Research and Analysis CentreStatistics CanadaOttawaCanada

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