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There are 27679 results for: content related to: Coping with high dimensionality in massive datasets

  1. You have free access to this content
    Sure independence screening for ultrahigh dimensional feature space

    Journal of the Royal Statistical Society: Series B (Statistical Methodology)

    Volume 70, Issue 5, November 2008, Pages: 849–911, Jianqing Fan and Jinchi Lv

    Version of Record online : 3 OCT 2008, DOI: 10.1111/j.1467-9868.2008.00674.x

  2. High Dimensionality in Large Datasets: Part I

    Wilmott

    Volume 2014, Issue 71, May 2014, Pages: 50–53, Sri Krishnamurthy

    Version of Record online : 22 MAY 2014, DOI: 10.1002/wilm.10328

  3. You have free access to this content
    References

    Multivariate Observations

    G. A. F. Seber, Pages: 626–670, 2008

    Published Online : 27 MAY 2008, DOI: 10.1002/9780470316641.refs

  4. Variance estimation using refitted cross-validation in ultrahigh dimensional regression

    Journal of the Royal Statistical Society: Series B (Statistical Methodology)

    Volume 74, Issue 1, January 2012, Pages: 37–65, Jianqing Fan, Shaojun Guo and Ning Hao

    Version of Record online : 10 OCT 2011, DOI: 10.1111/j.1467-9868.2011.01005.x

  5. Close Encounters of the 3D Kind – Exploiting High Dimensionality in Molecular Semiconductors

    Advanced Materials

    Volume 25, Issue 13, April 4, 2013, Pages: 1948–1954, Peter J. Skabara, Jean-Baptiste Arlin and Yves H. Geerts

    Version of Record online : 10 MAY 2012, DOI: 10.1002/adma.201200862

  6. On two-way Bayesian agglomerative clustering of gene expression data

    Statistical Analysis and Data Mining: The ASA Data Science Journal

    Volume 5, Issue 5, October 2012, Pages: 463–476, Anna Fowler and Nicholas A. Heard

    Version of Record online : 4 SEP 2012, DOI: 10.1002/sam.11162

  7. A survey on unsupervised outlier detection in high-dimensional numerical data

    Statistical Analysis and Data Mining: The ASA Data Science Journal

    Volume 5, Issue 5, October 2012, Pages: 363–387, Arthur Zimek, Erich Schubert and Hans-Peter Kriegel

    Version of Record online : 27 AUG 2012, DOI: 10.1002/sam.11161

  8. Variable Selection for Clustering with Gaussian Mixture Models

    Biometrics

    Volume 65, Issue 3, September 2009, Pages: 701–709, Cathy Maugis, Gilles Celeux and Marie-Laure Martin-Magniette

    Version of Record online : 5 FEB 2009, DOI: 10.1111/j.1541-0420.2008.01160.x

  9. SCAD-penalized quantile regression for high-dimensional data analysis and variable selection

    Statistica Neerlandica

    Volume 69, Issue 3, August 2015, Pages: 212–235, Muhammad Amin, Lixin Song, Milton Abdul Thorlie and Xiaoguang Wang

    Version of Record online : 26 JAN 2015, DOI: 10.1111/stan.12056

  10. Using distance covariance for improved variable selection with application to learning genetic risk models

    Statistics in Medicine

    Volume 34, Issue 10, 10 May 2015, Pages: 1708–1720, Jing Kong, Sijian Wang and Grace Wahba

    Version of Record online : 29 JAN 2015, DOI: 10.1002/sim.6441

  11. High dimensional variable selection via tilting

    Journal of the Royal Statistical Society: Series B (Statistical Methodology)

    Volume 74, Issue 3, June 2012, Pages: 593–622, Haeran Cho and Piotr Fryzlewicz

    Version of Record online : 15 FEB 2012, DOI: 10.1111/j.1467-9868.2011.01023.x

  12. High dimensional thresholded regression and shrinkage effect

    Journal of the Royal Statistical Society: Series B (Statistical Methodology)

    Volume 76, Issue 3, June 2014, Pages: 627–649, Zemin Zheng, Yingying Fan and Jinchi Lv

    Version of Record online : 7 NOV 2013, DOI: 10.1111/rssb.12037

  13. High-Dimensional Variable Selection in Meta-Analysis for Censored Data

    Biometrics

    Volume 67, Issue 2, June 2011, Pages: 504–512, Fei Liu, David Dunson and Fei Zou

    Version of Record online : 5 AUG 2010, DOI: 10.1111/j.1541-0420.2010.01466.x

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    VARIABLE SCREENING FOR CLUSTER ANALYSIS

    ETS Research Report Series

    Volume 1994, Issue 2, December 1994, Pages: i–55, John R. Donoghue

    Version of Record online : 8 AUG 2014, DOI: 10.1002/j.2333-8504.1994.tb01609.x

  15. You have free access to this content
    References

    Methods for Statistical Data Analysis of Multivariate Observations, Second Edition

    R. Gnanadesikan, Pages: 319–331, 2011

    Published Online : 28 JAN 2011, DOI: 10.1002/9781118032671.refs

  16. High dimensional ordinary least squares projection for screening variables

    Journal of the Royal Statistical Society: Series B (Statistical Methodology)

    Volume 78, Issue 3, June 2016, Pages: 589–611, Xiangyu Wang and Chenlei Leng

    Version of Record online : 8 NOV 2015, DOI: 10.1111/rssb.12127

  17. Sequential sufficient dimension reduction for large p, small n problems

    Journal of the Royal Statistical Society: Series B (Statistical Methodology)

    Volume 77, Issue 4, September 2015, Pages: 879–892, Xiangrong Yin and Haileab Hilafu

    Version of Record online : 7 NOV 2014, DOI: 10.1111/rssb.12093

  18. Measurement of fracton dimensionality in liquids by four-photon Rayleigh-wing spectroscopy

    Journal of Raman Spectroscopy

    Volume 37, Issue 6, June 2006, Pages: 693–696, A. F. Bunkin, A. P. Gorchakov, A. A. Nurmatov and S. M. Pershin

    Version of Record online : 5 MAY 2006, DOI: 10.1002/jrs.1530

  19. Variable selection in the presence of missing data: imputation-based methods

    Wiley Interdisciplinary Reviews: Computational Statistics

    Volume 9, Issue 5, September/October 2017, Yize Zhao and Qi Long

    Version of Record online : 24 MAY 2017, DOI: 10.1002/wics.1402

  20. Bayesian hierarchical structured variable selection methods with application to molecular inversion probe studies in breast cancer

    Journal of the Royal Statistical Society: Series C (Applied Statistics)

    Volume 63, Issue 4, August 2014, Pages: 595–620, Lin Zhang, Veerabhadran Baladandayuthapani, Bani K. Mallick, Ganiraju C. Manyam, Patricia A. Thompson, Melissa L. Bondy and Kim-Anh Do

    Version of Record online : 10 MAR 2014, DOI: 10.1111/rssc.12053