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Computation of Restricted Maximum-penalized-likelihood Estimates in Hidden Markov Models

Tero Aittokallio, Olli Nevalainen, Jussi Tolvi, Kalle Lertola, Esa Uusipaikka, Computation of Restricted Maximum-penalized-likelihood Estimates in Hidden Markov Models. TUCS Technical Reports 380, Turku Centre for Computer Science, 2000.

Abstract:

The maximum-penalized-likelihood estimation for hidden Markov models with general observation densities is described. All statistical inference, including the model estimation, testing, and selection, is based on the restricted optimization of the penalized likelihood function with respect to the chosen model family. The method is used in an economic application, where stock market index returns are modeled with hidden Markov models. Special emphasis is placed on modeling isolated outliers in the data, which has usually been ignored in previous research. The chosen model fits the data well, and is capable of modeling the outliers as well as a structural change in the series.

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BibTeX entry:

@TECHREPORT{tAiNeToLeUu00a,
  title = {Computation of Restricted Maximum-penalized-likelihood Estimates in Hidden Markov Models},
  author = {Aittokallio, Tero and Nevalainen, Olli and Tolvi, Jussi and Lertola, Kalle and Uusipaikka, Esa},
  number = {380},
  series = {TUCS Technical Reports},
  publisher = {Turku Centre for Computer Science},
  year = {2000},
  keywords = {hidden Markov model, likelihood inference, parameter estimation, model selection, stock markets, volatility, outliers},
  ISBN = {952-12-0761-2},
}

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