GENERALIZED CONSTELLATION GRAPH TRANSFORMATION MODEL FOR PREDICTION

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概要

• 論文の詳細を見る

• The Constellation Graph model (Sugano et al., 1988) and Trigonometric Series transformation model (Sugano, 1994) are proposed by a transformation of the explanatory variable space. However, those models are not linear with respect to parameters. So, the approximate values of the parameters are determined by the use of the Monte Carlo method. In this paper, we determine the parameters by the use of the alternative least square method for the model omitting the restriction of the weights. The model to obtain a predicted value utilizing weighted higher moments is called the Generalized Constellation Graph Transformation Model or GCGTM for short. We describe themethod in detail and show some examples of application to air pollution data.

• 日本計算機統計学会の論文

著者

• Sugano Osamu

  Tsuyama Commercial High School

関連論文

• CONSTELLATION GRAPH MODEL FOR PREDICTION
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• ON THE PERMUTATION DISTRIBUTION OF THE RANK PRODUCT-MOMENT STATISTIC
• GENERALIZED CONSTELLATION GRAPH TRANSFORMATION MODEL FOR PREDICTION
• RANK ASSOCIATION MEASURES FOR CONTINGENCY TABLES(Categorical Data Analysis)

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