Missing data analysis with sufficient dimension reduction-Reference-Cited by-同舟云学术

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Missing data analysis with sufficient dimension reduction

Published:2022-05-11 Issue:2 Volume:51 Page:630-651
ISSN:0319-5724
Container-title:Canadian Journal of Statistics
language:en
Short-container-title:Can J Statistics

Author:

Zheng Siming¹²,Wan Alan T. K.²,Zhou Yong³⁴^ORCID

Affiliation:

1. Academy of Mathematics and System Sciences Chinese Academy of Science Beijing China

2. Department of Management Sciences City University of Hong Kong Hong Kong China

3. Key Laboratory of Advanced Theory and Application in Statistics and Data Science, MOE Shanghai China

4. Academy of Statistics and Interdisciplinary Sciences East China Normal University Shanghai China

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cjs.11700

Reference46 articles.

1. Bierens H. J.(1987).Kernel estimators of regression functions. In Bewley T. (Ed.) Advances in Econometrics: Fifth World Congress Vol. 1 Cambridge University Press Cambridge 99–144.

2. Pseudoempirical-likelihood-based method using calibration for longitudinal data with dropout

3. Efficient Quantile Regression Analysis With Missing Observations

4. Graphics for Regressions with a Binary Response

5. Regression Graphics

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