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Regression Models for Binary Time Series

  • Benjamin Kedem
  • Konstantinos Fokianos
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 46)

Abstract

We consider the general regression problem for binary time series where the covariates are stochastic and time dependent and the inverse link is any differentiable cumulative distribution function. This means that the popular logistic and probit regression models are special cases. The statistical analysis is carried out via partial likelihood estimation. Under a certain large sample assumption on the covariates, and owing to the fact that the score process is a martingale, the maximum partial likelihood estimator is consistent and asymptotically normal. From this we obtain the asymptotic distribution of a certain useful goodness of fit statistic.

Keywords

Regression Model Partial Likelihood General Regression Model Categorical Time Series Probit Regression Model 
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

© Springer Science + Business Media, Inc. 2002

Authors and Affiliations

  • Benjamin Kedem
    • 1
  • Konstantinos Fokianos
    • 2
  1. 1.Department of MathematicsUniversity of MarylandCollege ParkUSA
  2. 2.Department of Mathematics & StatisticsUniversity of CyprusNikosiaCyprus

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