Communications for Statistical Applications and Methods

  • 제8권1호
  • /
  • Pages.173-184
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  • 2001
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  • 2287-7843(pISSN)
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  • 2383-4757(eISSN)
한국통계학회 (The Korean Statistical Society)
권호 표지

A Penalized Likelihood Method for Model Complexity

  • Ahn, Sung M. (School of Management Information Systems, Kookmin University)
  • 발행 : 2001.04.01
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초록

We present an algorithm for the complexity reduction of a general Gaussian mixture model by using a penalized likelihood method. One of our important assumptions is that we begin with an overfitted model in terms of the number of components. So our main goal is to eliminate redundant components in the overfitted model. As shown in the section of simulation results, the algorithm works well with the selected densities.

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참고문헌

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