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There are 29955 results for: content related to: Selection of ordinally scaled independent variables with applications to international classification of functioning core sets

  1. The group lasso for logistic regression

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

    Volume 70, Issue 1, February 2008, Pages: 53–71, Lukas Meier, Sara Van De Geer and Peter Bühlmann

    Version of Record online : 4 JAN 2008, DOI: 10.1111/j.1467-9868.2007.00627.x

  2. Model selection and estimation in regression with grouped variables

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

    Volume 68, Issue 1, February 2006, Pages: 49–67, Ming Yuan and Yi Lin

    Version of Record online : 21 DEC 2005, DOI: 10.1111/j.1467-9868.2005.00532.x

  3. 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

  4. Strong rules for discarding predictors in lasso-type problems

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

    Volume 74, Issue 2, March 2012, Pages: 245–266, Robert Tibshirani, Jacob Bien, Jerome Friedman, Trevor Hastie, Noah Simon, Jonathan Taylor and Ryan J. Tibshirani

    Version of Record online : 3 NOV 2011, DOI: 10.1111/j.1467-9868.2011.01004.x

  5. You have free access to this content
    Statistical validation of the brief International Classification of Functioning, Disability and Health Core Set for osteoarthritis based on a large international sample of patients with osteoarthritis

    Arthritis Care & Research

    Volume 65, Issue 2, February 2013, Pages: 177–186, Cornelia Oberhauser, Reuben Escorpizo, Annelies Boonen, Gerold Stucki and Alarcos Cieza

    Version of Record online : 30 JAN 2013, DOI: 10.1002/acr.21775

  6. 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

  7. The joint graphical lasso for inverse covariance estimation across multiple classes

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

    Volume 76, Issue 2, March 2014, Pages: 373–397, Patrick Danaher, Pei Wang and Daniela M. Witten

    Version of Record online : 12 AUG 2013, DOI: 10.1111/rssb.12033

  8. Sparse additive models

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

    Volume 71, Issue 5, November 2009, Pages: 1009–1030, Pradeep Ravikumar, John Lafferty, Han Liu and Larry Wasserman

    Version of Record online : 19 OCT 2009, DOI: 10.1111/j.1467-9868.2009.00718.x

  9. A group lasso approach for non-stationary spatial–temporal covariance estimation

    Environmetrics

    Volume 23, Issue 1, February 2012, Pages: 12–23, Nan-Jung Hsu, Ya-Mei Chang and Hsin-Cheng Huang

    Version of Record online : 31 AUG 2011, DOI: 10.1002/env.1130

  10. Variable selection in linear models

    Wiley Interdisciplinary Reviews: Computational Statistics

    Volume 6, Issue 1, January/February 2014, Pages: 1–9, Yuqi Chen, Pang Du and Yuedong Wang

    Version of Record online : 13 DEC 2013, DOI: 10.1002/wics.1284

  11. The group exponential lasso for bi-level variable selection

    Biometrics

    Volume 71, Issue 3, September 2015, Pages: 731–740, Patrick Breheny

    Version of Record online : 13 MAR 2015, DOI: 10.1111/biom.12300

  12. Learning out of leaders

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

    Volume 74, Issue 3, June 2012, Pages: 475–513, Mathilde Mougeot, Dominique Picard and Karine Tribouley

    Version of Record online : 16 MAR 2012, DOI: 10.1111/j.1467-9868.2011.01024.x

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    Group variable selection via convex log-exp-sum penalty with application to a breast cancer survivor study

    Biometrics

    Volume 71, Issue 1, March 2015, Pages: 53–62, Zhigeng Geng, Sijian Wang, Menggang Yu, Patrick O. Monahan, Victoria Champion and Grace Wahba

    Version of Record online : 24 SEP 2014, DOI: 10.1111/biom.12230

  14. Multivariate sparse group lasso for the multivariate multiple linear regression with an arbitrary group structure

    Biometrics

    Volume 71, Issue 2, June 2015, Pages: 354–363, Yanming Li, Bin Nan and Ji Zhu

    Version of Record online : 2 MAR 2015, DOI: 10.1111/biom.12292

  15. Regularized Rare Variant Enrichment Analysis for Case-Control Exome Sequencing Data

    Genetic Epidemiology

    Volume 38, Issue 2, February 2014, Pages: 104–113, Nicholas B. Larson and Daniel J. Schaid

    Version of Record online : 30 DEC 2013, DOI: 10.1002/gepi.21783

  16. Variable selection via the weighted group lasso for factor analysis models

    Canadian Journal of Statistics

    Volume 40, Issue 2, June 2012, Pages: 345–361, Kei Hirose and Sadanori Konishi

    Version of Record online : 17 MAY 2012, DOI: 10.1002/cjs.11129

  17. 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

  18. A network analysis of the volatility of high dimensional financial series

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

    Volume 66, Issue 3, April 2017, Pages: 581–605, Matteo Barigozzi and Marc Hallin

    Version of Record online : 17 SEP 2016, DOI: 10.1111/rssc.12177

  19. Survival prediction and variable selection with simultaneous shrinkage and grouping priors

    Statistical Analysis and Data Mining: The ASA Data Science Journal

    Volume 8, Issue 2, April 2015, Pages: 114–127, Kyu Ha Lee, Sounak Chakraborty and Jianguo Sun

    Version of Record online : 22 APR 2015, DOI: 10.1002/sam.11266

  20. Regression shrinkage and selection via the lasso: a retrospective

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

    Volume 73, Issue 3, June 2011, Pages: 273–282, Robert Tibshirani

    Version of Record online : 20 APR 2011, DOI: 10.1111/j.1467-9868.2011.00771.x