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Grace Wahba

Personal Details

First Name:Grace
Middle Name:
Last Name:Wahba
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RePEc Short-ID:pwa139
[This author has chosen not to make the email address public]
http://www.stat.wisc.edu/~wahba

Affiliation

University of Wisconsin -> Statistics Department

http://www.stat.wisc.edu/
Madison Wisconsin USA

Research output

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Jump to: Articles

Articles

  1. Zhigeng Geng & Sijian Wang & Menggang Yu & Patrick O. Monahan & Victoria Champion & Grace Wahba, 2015. "Group variable selection via convex log-exp-sum penalty with application to a breast cancer survivor study," Biometrics, The International Biometric Society, vol. 71(1), pages 53-62, March.
  2. Lee, Yoonkyung & Lin, Yi & Wahba, Grace, 2004. "Multicategory Support Vector Machines: Theory and Application to the Classification of Microarray Data and Satellite Radiance Data," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 67-81, January.
  3. Yuan, Ming & Wahba, Grace, 2004. "Doubly penalized likelihood estimator in heteroscedastic regression," Statistics & Probability Letters, Elsevier, vol. 69(1), pages 11-20, August.
  4. Hao Helen Zhang & Grace Wahba & Yi Lin & Meta Voelker & Michael Ferris & Ronald Klein & Barbara Klein, 2004. "Variable Selection and Model Building via Likelihood Basis Pursuit," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 659-672, January.
  5. Gao F. & Wahba G. & Klein R. & Klein B., 2001. "Smoothing Spline ANOVA for Multivariate Bernoulli Observations With Application to Ophthalmology Data," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 127-160, March.
  6. Wahba, Grace & Wang, Yuedong, 1995. "Behavior near zero of the distribution of GCV smoothing parameter estimates," Statistics & Probability Letters, Elsevier, vol. 25(2), pages 105-111, November.
  7. Bates, Douglas & Reames, Fred & Wahba, Grace, 1993. "Getting better contour plots with S and GCVPACK," Computational Statistics & Data Analysis, Elsevier, vol. 15(3), pages 329-342, March.
  8. Gu, Chong & Heckman, Nancy & Wahba, Grace, 1992. "A note on generalized cross-validation with replicates," Statistics & Probability Letters, Elsevier, vol. 14(4), pages 283-287, July.
  9. Wahba, Grace, 1969. "Estimation of the Coefficients in a Multidimensional Distributed Lag Model," Econometrica, Econometric Society, vol. 37(3), pages 398-407, July.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Articles

  1. Zhigeng Geng & Sijian Wang & Menggang Yu & Patrick O. Monahan & Victoria Champion & Grace Wahba, 2015. "Group variable selection via convex log-exp-sum penalty with application to a breast cancer survivor study," Biometrics, The International Biometric Society, vol. 71(1), pages 53-62, March.

    Cited by:

    1. Mallick, Himel & Yi, Nengjun, 2017. "Bayesian group bridge for bi-level variable selection," Computational Statistics & Data Analysis, Elsevier, vol. 110(C), pages 115-133.

  2. Lee, Yoonkyung & Lin, Yi & Wahba, Grace, 2004. "Multicategory Support Vector Machines: Theory and Application to the Classification of Microarray Data and Satellite Radiance Data," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 67-81, January.

    Cited by:

    1. Liu, Yufeng & Helen Zhang, Hao & Park, Cheolwoo & Ahn, Jeongyoun, 2007. "Support vector machines with adaptive Lq penalty," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 6380-6394, August.
    2. Fang Yao & Yichao Wu & Jialin Zou, 2016. "Probability-enhanced effective dimension reduction for classifying sparse functional data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(1), pages 1-22, March.
    3. Keiji Tatsumi & Tetsuzo Tanino, 2014. "Support vector machines maximizing geometric margins for multi-class classification," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(3), pages 815-840, October.
    4. Park, Changyi & Koo, Ja-Yong & Kim, Peter T. & Lee, Jae Won, 2008. "Stepwise feature selection using generalized logistic loss," Computational Statistics & Data Analysis, Elsevier, vol. 52(7), pages 3709-3718, March.
    5. Xingye Qiao & Yufeng Liu, 2009. "Adaptive Weighted Learning for Unbalanced Multicategory Classification," Biometrics, The International Biometric Society, vol. 65(1), pages 159-168, March.
    6. Hoai An Le Thi & Manh Cuong Nguyen, 2017. "DCA based algorithms for feature selection in multi-class support vector machine," Annals of Operations Research, Springer, vol. 249(1), pages 273-300, February.
    7. Yoonkyung Lee, 2014. "Comments on: Support vector machines maximizing geometric margins for multi-class classification," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(3), pages 852-855, October.
    8. Fang Yao & Yichao Wu & Jialin Zou, 2016. "Probability-enhanced effective dimension reduction for classifying sparse functional data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(1), pages 1-22, March.
    9. Gardner-Lubbe, Sugnet, 2016. "A triplot for multiclass classification visualisation," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 20-32.
    10. Fu, Sheng & Zhang, Sanguo & Liu, Yufeng, 2018. "Adaptively weighted large-margin angle-based classifiers," Journal of Multivariate Analysis, Elsevier, vol. 166(C), pages 282-299.
    11. Aijun Yang & Yunxian Li & Niansheng Tang & Jinguan Lin, 2015. "Bayesian variable selection in multinomial probit model for classifying high-dimensional data," Computational Statistics, Springer, vol. 30(2), pages 399-418, June.
    12. Safo, Sandra E. & Ahn, Jeongyoun, 2016. "General sparse multi-class linear discriminant analysis," Computational Statistics & Data Analysis, Elsevier, vol. 99(C), pages 81-90.
    13. Song Huang & Tiejun Tong & Hongyu Zhao, 2010. "Bias-Corrected Diagonal Discriminant Rules for High-Dimensional Classification," Biometrics, The International Biometric Society, vol. 66(4), pages 1096-1106, December.
    14. Chakraborty, Sounak & Guo, Ruixin, 2011. "A Bayesian hybrid Huberized support vector machine and its applications in high-dimensional medical data," Computational Statistics & Data Analysis, Elsevier, vol. 55(3), pages 1342-1356, March.
    15. Pedro Duarte Silva, A., 2017. "Optimization approaches to Supervised Classification," European Journal of Operational Research, Elsevier, vol. 261(2), pages 772-788.
    16. van den Burg, G.J.J. & Groenen, P.J.F., 2014. "GenSVM: A Generalized Multiclass Support Vector Machine," Econometric Institute Research Papers EI 2014-33, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    17. Peter Hall & D. M. Titterington & Jing-Hao Xue, 2009. "Tilting methods for assessing the influence of components in a classifier," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(4), pages 783-803.
    18. Yann Guermeur, 2014. "Comments on: Support Vector Machines Maximizing Geometric Margins for Multi-class Classification," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(3), pages 844-851, October.

  3. Hao Helen Zhang & Grace Wahba & Yi Lin & Meta Voelker & Michael Ferris & Ronald Klein & Barbara Klein, 2004. "Variable Selection and Model Building via Likelihood Basis Pursuit," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 659-672, January.

    Cited by:

    1. Diego Vidaurre & Concha Bielza & Pedro Larrañaga, 2013. "A Survey of L 1 Regression," International Statistical Review, International Statistical Institute, vol. 81(3), pages 361-387, December.
    2. Choi, Hosik & Yeo, Donghwa & Kwon, Sunghoon & Kim, Yongdai, 2011. "Gene selection and prediction for cancer classification using support vector machines with a reject option," Computational Statistics & Data Analysis, Elsevier, vol. 55(5), pages 1897-1908, May.
    3. Zhimeng Sun & Zhi Su & Jingyi Ma, 2014. "Focused vector information criterion model selection and model averaging regression with missing response," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 77(3), pages 415-432, April.
    4. Avalos, Marta & Grandvalet, Yves & Ambroise, Christophe, 2007. "Parsimonious additive models," Computational Statistics & Data Analysis, Elsevier, vol. 51(6), pages 2851-2870, March.
    5. Peter Bickel & Bo Li & Alexandre Tsybakov & Sara Geer & Bin Yu & Teófilo Valdés & Carlos Rivero & Jianqing Fan & Aad Vaart, 2006. "Regularization in statistics," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 15(2), pages 271-344, September.
    6. Lee, Sangin & Kwon, Sunghoon & Kim, Yongdai, 2016. "A modified local quadratic approximation algorithm for penalized optimization problems," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 275-286.
    7. Sang Han Lee & Johan Lim & Marina Vannucci & Eva Petkova & Maurice Preter & Donald F. Klein, 2008. "Order-Preserving Dimension Reduction Procedure for the Dominance of Two Mean Curves with Application to Tidal Volume Curves," Biometrics, The International Biometric Society, vol. 64(3), pages 931-939, September.
    8. Zeng, Peng, 2011. "A link-free method for testing the significance of predictors," Journal of Multivariate Analysis, Elsevier, vol. 102(3), pages 550-562, March.

  4. Gao F. & Wahba G. & Klein R. & Klein B., 2001. "Smoothing Spline ANOVA for Multivariate Bernoulli Observations With Application to Ophthalmology Data," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 127-160, March.

    Cited by:

    1. Siem Jan Koopman & Soon Yip Wong, 2008. "Spline Smoothing over Difficult Regions," Tinbergen Institute Discussion Papers 08-114/4, Tinbergen Institute.

  5. Wahba, Grace & Wang, Yuedong, 1995. "Behavior near zero of the distribution of GCV smoothing parameter estimates," Statistics & Probability Letters, Elsevier, vol. 25(2), pages 105-111, November.

    Cited by:

    1. Wang, Yuedong, 1998. "Sample size calculations for smoothing splines based on Bayesian confidence intervals," Statistics & Probability Letters, Elsevier, vol. 38(2), pages 161-166, June.

  6. Wahba, Grace, 1969. "Estimation of the Coefficients in a Multidimensional Distributed Lag Model," Econometrica, Econometric Society, vol. 37(3), pages 398-407, July.

    Cited by:

    1. E. Philip Howrey, 1980. "The Role of Time Series Analysis in Econometric Model Evaluation," NBER Chapters,in: Evaluation of Econometric Models, pages 275-307 National Bureau of Economic Research, Inc.

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