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Approximating conditional density functions using dimension reduction

Fan, Jian-qing, Peng, Liang, Yao, Qiwei ORCID: 0000-0003-2065-8486 and Zhang, Wenyang (2009) Approximating conditional density functions using dimension reduction. Acta Mathematicae Applicatae Sinica, English Series, 25 (3). pp. 445-456. ISSN 0168-9673

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Identification Number: 10.1007/s10255-008-8815-1


We propose to approximate the conditional density function of a random variable Y given a dependent random d-vector X by that of Y given θ τ X, where the unit vector θ is selected such that the average Kullback-Leibler discrepancy distance between the two conditional density functions obtains the minimum. Our approach is nonparametric as far as the estimation of the conditional density functions is concerned. We have shown that this nonparametric estimator is asymptotically adaptive to the unknown index θ in the sense that the first order asymptotic mean squared error of the estimator is the same as that when θ was known. The proposed method is illustrated using both simulated and real-data examples.

Item Type: Article
Official URL:
Additional Information: © 2009 Springer Science+Business Media
Divisions: Statistics
Subjects: Q Science > QA Mathematics
Date Deposited: 26 Jan 2011 11:39
Last Modified: 16 May 2024 00:56

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