欠損混合分布モデルとその応用

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  • Missing Region Modeling and the Multivariate Normal Mixture Model
  • ケッソン コンゴウ ブンプ モデル ト ソノ オウヨウ

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A dataset that contains missing regions is assumed to arise from two or more populations. In order to decompose the data into meaningful component distributions, a normal mixture model can be applied. A problem with this approach is that the estimated parameters are biased by fitting the standard normal mixture model. To correct the bias, a log-likelihood function for missing region probabilities is constructed, and the maximum likelihood estimators of the parameters - i.e. mix-ing proportions, mean vectors, and variance-covariance matrixes - are derived in the context of the EM algorithm. The performance of the model is verified by numerical experiments, and the model is applied to plasma velocity data.

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