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数理人口学・数理生物学セミナー
過去の記録 ～04/19｜次回の予定｜今後の予定 04/20～
2009年12月24日(木)
16:00-17:30 数理科学研究科棟(駒場) 123号室
堀内 四郎 氏 (The City University of New York, Hunter College)
Decomposition分析:趨勢データ分析の新しい枠組とアプローチ
http://shiro_horiuchi.homestead.com/homepage.html
堀内 四郎 氏 (The City University of New York, Hunter College)
Decomposition分析:趨勢データ分析の新しい枠組とアプローチ
[ 講演概要 ]
A demographic measure is often expressed as a deterministic or stochastic function of multiple variables (covariates), and a general problem (the decomposition problem) is to assess contributions of individual covariates to a difference in the demographic measure (dependent variable) between two populations.
We propose a method of decomposition analysis based on an assumption that covariates change continuously along an actual or hypothetical dimension. This assumption leads to a general model that logically justifi es the additivity of covariate effects and the elimination of interaction terms, even if the dependent variable itself is a nonadditive function.
A comparison with earlier methods illustrates other practical advantages of the method: in addition to an absence of residuals or interaction terms, the method can easily handle a large number of covariates and does not require a logically meaningful ordering of covariates. Two empirical examples show that the method can be applied fl exibly to a wide variety of decomposition problems.
[ 参考URL ]A demographic measure is often expressed as a deterministic or stochastic function of multiple variables (covariates), and a general problem (the decomposition problem) is to assess contributions of individual covariates to a difference in the demographic measure (dependent variable) between two populations.
We propose a method of decomposition analysis based on an assumption that covariates change continuously along an actual or hypothetical dimension. This assumption leads to a general model that logically justifi es the additivity of covariate effects and the elimination of interaction terms, even if the dependent variable itself is a nonadditive function.
A comparison with earlier methods illustrates other practical advantages of the method: in addition to an absence of residuals or interaction terms, the method can easily handle a large number of covariates and does not require a logically meaningful ordering of covariates. Two empirical examples show that the method can be applied fl exibly to a wide variety of decomposition problems.
http://shiro_horiuchi.homestead.com/homepage.html
