IDEAS home Printed from https://ideas.repec.org/a/bla/jorssb/v78y2016i4p849-867.html
   My bibliography  Save this article

Distance shrinkage and Euclidean embedding via regularized kernel estimation

Author

Listed:
  • Luwan Zhang
  • Grace Wahba
  • Ming Yuan

Abstract

No abstract is available for this item.

Suggested Citation

  • Luwan Zhang & Grace Wahba & Ming Yuan, 2016. "Distance shrinkage and Euclidean embedding via regularized kernel estimation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(4), pages 849-867, September.
    • Handle: RePEc:bla:jorssb:v:78:y:2016:i:4:p:849-867
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/rssb.12138
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Chen, Lisha & Buja, Andreas, 2009. "Local Multidimensional Scaling for Nonlinear Dimension Reduction, Graph Drawing, and Proximity Analysis," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 209-219.
    2. Gale Young & A. Householder, 1938. "Discussion of a set of points in terms of their mutual distances," Psychometrika, Springer;The Psychometric Society, vol. 3(1), pages 19-22, March.
    3. Ming Yuan & Ali Ekici & Zhaosong Lu & Renato Monteiro, 2007. "Dimension reduction and coefficient estimation in multivariate linear regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(3), pages 329-346, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chen, Canyi & Xu, Wangli & Zhu, Liping, 2022. "Distributed estimation in heterogeneous reduced rank regression: With application to order determination in sufficient dimension reduction," Journal of Multivariate Analysis, Elsevier, vol. 190(C).
    2. Kuroda, Kaori & Hashiguchi, Hiroki & Fujiwara, Kantaro & Ikeguchi, Tohru, 2014. "Reconstruction of network structures from marked point processes using multi-dimensional scaling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 415(C), pages 194-204.
    3. Fengzhen Zhai & Qingna Li, 2020. "A Euclidean distance matrix model for protein molecular conformation," Journal of Global Optimization, Springer, vol. 76(4), pages 709-728, April.
    4. Luo, Chongliang & Liang, Jian & Li, Gen & Wang, Fei & Zhang, Changshui & Dey, Dipak K. & Chen, Kun, 2018. "Leveraging mixed and incomplete outcomes via reduced-rank modeling," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 378-394.
    5. Fujikoshi, Yasunori & Sakurai, Tetsuro, 2016. "High-dimensional consistency of rank estimation criteria in multivariate linear model," Journal of Multivariate Analysis, Elsevier, vol. 149(C), pages 199-212.
    6. François Bavaud, 2011. "On the Schoenberg Transformations in Data Analysis: Theory and Illustrations," Journal of Classification, Springer;The Classification Society, vol. 28(3), pages 297-314, October.
    7. Fan Cheng & Rob J Hyndman & Anastasios Panagiotelis, 2021. "Manifold Learning with Approximate Nearest Neighbors," Monash Econometrics and Business Statistics Working Papers 3/21, Monash University, Department of Econometrics and Business Statistics.
    8. Mishra, Aditya & Dey, Dipak K. & Chen, Yong & Chen, Kun, 2021. "Generalized co-sparse factor regression," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
    9. Pan Shang & Lingchen Kong, 2021. "Regularization Parameter Selection for the Low Rank Matrix Recovery," Journal of Optimization Theory and Applications, Springer, vol. 189(3), pages 772-792, June.
    10. Qiu, Yue & Zheng, Yuchen, 2023. "Improving box office projections through sentiment analysis: Insights from regularization-based forecast combinations," Economic Modelling, Elsevier, vol. 125(C).
    11. W. Alan Nicewander & Joseph Lee Rodgers, 2022. "Obituary: Bruce McArthur Bloxom 1938–2020," Psychometrika, Springer;The Psychometric Society, vol. 87(3), pages 1042-1044, September.
    12. Panpan Yu & Qingna Li, 2018. "Ordinal Distance Metric Learning with MDS for Image Ranking," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 35(01), pages 1-19, February.
    13. Chao Ding & Hou-Duo Qi, 2017. "Convex Euclidean distance embedding for collaborative position localization with NLOS mitigation," Computational Optimization and Applications, Springer, vol. 66(1), pages 187-218, January.
    14. Jin Liu & Jian Huang & Shuangge Ma, 2012. "Analysis of Genome-Wide Association Studies with Multiple Outcomes Using Penalization," PLOS ONE, Public Library of Science, vol. 7(12), pages 1-12, December.
    15. Härdle, Wolfgang Karl & Huang, Chen & Chao, Shih-Kang, 2016. "Factorisable sparse tail event curves with expectiles," SFB 649 Discussion Papers 2016-018, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    16. Lorenzo Carbonari & Alessio Farcomeni & Cosimo Petracchi & Giovanni Trovato, 2024. "Macroprudential Policies and Credit Volatility," Working Paper series 24-16, Rimini Centre for Economic Analysis.
    17. Arash Karimzadeh & Omidreza Shoghli & Sepehr Sabeti & Hamed Tabkhi, 2022. "Multi-Asset Defect Hotspot Prediction for Highway Maintenance Management: A Risk-Based Machine Learning Approach," Sustainability, MDPI, vol. 14(9), pages 1-27, April.
    18. Zha, Hongyuan & Zhang, Zhenyue, 2007. "Continuum Isomap for manifold learnings," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 184-200, September.
    19. Cornelius Fritz & Göran Kauermann, 2022. "On the interplay of regional mobility, social connectedness and the spread of COVID‐19 in Germany," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(1), pages 400-424, January.
    20. Fanhua Shang & Yuanyuan Liu & Fanjie Shang & Hongying Liu & Lin Kong & Licheng Jiao, 2020. "A Unified Scalable Equivalent Formulation for Schatten Quasi-Norms," Mathematics, MDPI, vol. 8(8), pages 1-19, August.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:jorssb:v:78:y:2016:i:4:p:849-867. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.