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Direct importance estimation for covariate shift adaptation

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Listed:
  • Masashi Sugiyama
  • Taiji Suzuki
  • Shinichi Nakajima
  • Hisashi Kashima
  • Paul Bünau
  • Motoaki Kawanabe

Abstract

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Suggested Citation

  • Masashi Sugiyama & Taiji Suzuki & Shinichi Nakajima & Hisashi Kashima & Paul Bünau & Motoaki Kawanabe, 2008. "Direct importance estimation for covariate shift adaptation," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 60(4), pages 699-746, December.
    • Handle: RePEc:spr:aistmt:v:60:y:2008:i:4:p:699-746
    DOI: 10.1007/s10463-008-0197-x
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    References listed on IDEAS

    as
    1. Sugiyama Masashi & Müller Klaus-Robert, 2005. "Input-dependent estimation of generalization error under covariate shift," Statistics & Risk Modeling, De Gruyter, vol. 23(4), pages 249-279, April.
    2. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
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    Citations

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    Cited by:

    1. Zhexiao Lin & Peng Ding & Fang Han, 2023. "Estimation Based on Nearest Neighbor Matching: From Density Ratio to Average Treatment Effect," Econometrica, Econometric Society, vol. 91(6), pages 2187-2217, November.
    2. Dylan Brewer & Alyssa Carlson, 2024. "Addressing sample selection bias for machine learning methods," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(3), pages 383-400, April.
    3. Jiaming Mao & Zhesheng Zheng, 2020. "Structural Regularization," Papers 2004.12601, arXiv.org, revised Jun 2020.
    4. Hofer, Vera, 2015. "Adapting a classification rule to local and global shift when only unlabelled data are available," European Journal of Operational Research, Elsevier, vol. 243(1), pages 177-189.
    5. Nishiyama, Tomohiro, 2019. "A New Lower Bound for Kullback-Leibler Divergence Based on Hammersley-Chapman-Robbins Bound," OSF Preprints wa98j, Center for Open Science.
    6. Yukitoshi Matsushita & Taisuke Otsu & Keisuke Takahata, 2022. "Estimating density ratio of marginals to joint: Applications to causal inference," STICERD - Econometrics Paper Series 619, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    7. Masahiro Kato & Masatoshi Uehara & Shota Yasui, 2020. "Off-Policy Evaluation and Learning for External Validity under a Covariate Shift," Papers 2002.11642, arXiv.org, revised Oct 2020.
    8. Sheng, Haiyang & Yu, Guan, 2023. "TNN: A transfer learning classifier based on weighted nearest neighbors," Journal of Multivariate Analysis, Elsevier, vol. 193(C).
    9. Jiaming Mao & Jingzhi Xu, 2020. "Ensemble Learning with Statistical and Structural Models," Papers 2006.05308, arXiv.org.
    10. Byol Kim & Song Liu & Mladen Kolar, 2021. "Two‐sample inference for high‐dimensional Markov networks," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(5), pages 939-962, November.
    11. Masashi Sugiyama & Taiji Suzuki & Takafumi Kanamori, 2012. "Density-ratio matching under the Bregman divergence: a unified framework of density-ratio estimation," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 64(5), pages 1009-1044, October.
    12. Yannis Yatracos, 2013. "Equal percent bias reduction and variance proportionate modifying properties with mean–covariance preserving matching," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 65(1), pages 69-87, February.
    13. Abdolnasser Sadeghkhani & Yingwei Peng & Chunfang Devon Lin, 2019. "A Parametric Bayesian Approach in Density Ratio Estimation," Stats, MDPI, vol. 2(2), pages 1-13, March.
    14. Masahiro Kato & Hikaru Kawarazaki, 2019. "Model Specification Test with Unlabeled Data: Approach from Covariate Shift," Papers 1911.00688, arXiv.org, revised Feb 2020.
    15. Sookyo Jeong & Hongseok Namkoong, 2020. "Assessing External Validity Over Worst-case Subpopulations," Papers 2007.02411, arXiv.org, revised Feb 2022.

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