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A partially linear single-index transformation model and its nonparametric estimation

  • Published:2015-02-20 Issue:1 Volume:43 Page:97-117
  • ISSN:0319-5724
  • Container-title:Canadian Journal of Statistics
  • language:en
  • Short-container-title:Can. J. Statistics

Author:

Ding Xiaobo123,Zhou Xiao-Hua23,Wang Qihua14

Affiliation:

1. Academy of Mathematics and Systems Science; Chinese Academy of Sciences; Beijing China

2. HSR&D Center of Excellence; Veterans Affairs Puget Sound Health Care System; Seattle WA U.S.A.

3. Department of Biostatistics; University of Washington; Seattle WA U.S.A.

4. Institute of Statistical Science; Shenzhen University; Shenzhen Guangdong China

Funder

U.S. Department of Veterans Affairs

Publisher

Wiley

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Reference27 articles.

1. An analysis of transformations revisited;Bickel;Journal of the American Statistical Association,1981

2. An analysis of transformations (with discussion);Box;Journal of the Royal Statistical Society, Series B (Methodological),1964

3. Estimating optimal transformations for multiple regression and correlation;Breiman;Journal of the American Statistical Association,1985

4. Generalized partially linear single-index models;Carroll;Journal of the American Statistical Association,1997

5. Rank estimations of transformation models;Chen;Econometrica,2002

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Robust estimation for partial linear single-index models;Journal of Nonparametric Statistics;2022-01-02

2. Estimation of the error distribution function for partial linear single-index models;Communications in Statistics - Simulation and Computation;2018-12-04

3. Estimation and hypothesis test for single-index multiplicative models;TEST;2018-05-03

4. Partial linear single-index models with additive distortion measurement errors;Communications in Statistics - Theory and Methods;2017-02-17

5. Partially linear estimation using sufficient dimension reduction;ESAIM: Probability and Statistics;2016
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