About: Sufficient dimension reduction

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In statistics, sufficient dimension reduction (SDR) is a paradigm for analyzing data that combines the ideas of dimension reduction with the concept of sufficiency. Dimension reduction has long been a primary goal of regression analysis. Given a response variable y and a p-dimensional predictor vector , regression analysis aims to study the distribution of , the conditional distribution of given . A dimension reduction is a function that maps to a subset of , k < p, thereby reducing the dimension of . For example, may be one or more linear combinations of .

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  • In statistics, sufficient dimension reduction (SDR) is a paradigm for analyzing data that combines the ideas of dimension reduction with the concept of sufficiency. Dimension reduction has long been a primary goal of regression analysis. Given a response variable y and a p-dimensional predictor vector , regression analysis aims to study the distribution of , the conditional distribution of given . A dimension reduction is a function that maps to a subset of , k < p, thereby reducing the dimension of . For example, may be one or more linear combinations of . A dimension reduction is said to be sufficient if the distribution of is the same as that of . In other words, no information about the regression is lost in reducing the dimension of if the reduction is sufficient. (en)
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  • In statistics, sufficient dimension reduction (SDR) is a paradigm for analyzing data that combines the ideas of dimension reduction with the concept of sufficiency. Dimension reduction has long been a primary goal of regression analysis. Given a response variable y and a p-dimensional predictor vector , regression analysis aims to study the distribution of , the conditional distribution of given . A dimension reduction is a function that maps to a subset of , k < p, thereby reducing the dimension of . For example, may be one or more linear combinations of . (en)
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  • Sufficient dimension reduction (en)
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