【摘 要】
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We employ the sliced inverse approach in conjunction with the factor analysis model to obtain a sufficient dimension reduction method, named sliced inverse
【出 处】
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IMS-China International Conference on Statistics and Probabi
论文部分内容阅读
We employ the sliced inverse approach in conjunction with the factor analysis model to obtain a sufficient dimension reduction method, named sliced inverse factor analysis (SIFA).This method is particularly useful when the number of predictors is substantially larger than the sample size.We show theoretically that SIFA is able to recover the latent factor accurately.In addition, a consistent BIC-typc selection criterion is developed to select the structure dimension.Both simulations and a microarray example arc presented to illustrate the usefulness of the new method.
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