Grace Wahba

Personal Details

First Name:	Grace
Middle Name:
Last Name:	Wahba
Suffix:
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

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. Yuan, Ming & Wahba, Grace, 2004. "Doubly penalized likelihood estimator in heteroscedastic regression," Statistics & Probability Letters, Elsevier, vol. 69(1), pages 11-20, August.

   Cited by:

   1. I. Gijbels & I. Prosdocimi, 2011. "Smooth estimation of mean and dispersion function in extended generalized additive models with application to Italian induced abortion data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(11), pages 2391-2411, December.

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. Ya-Ju Fan & Wanpracha Chaovalitwongse, 2010. "Optimizing feature selection to improve medical diagnosis," Annals of Operations Research, Springer, vol. 174(1), pages 169-183, February.
   2. Diego Vidaurre & Concha Bielza & Pedro Larrañaga, 2013. "A Survey of L1 Regression," International Statistical Review, International Statistical Institute, vol. 81(3), pages 361-387, December.
   3. 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.
   4. Kuangnan Fang & Xinyan Fan & Wei Lan & Bingquan Wang, 2019. "Nonparametric additive beta regression for fractional response with application to body fat data," Annals of Operations Research, Springer, vol. 276(1), pages 331-347, May.
   5. 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.
   6. Avalos, Marta & Grandvalet, Yves & Ambroise, Christophe, 2007. "Parsimonious additive models," Computational Statistics & Data Analysis, Elsevier, vol. 51(6), pages 2851-2870, March.
   7. 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.
   8. 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.
   9. 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.
   10. 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.
   11. Ding, Hui & Zhang, Jian & Zhang, Riquan, 2022. "Nonparametric variable screening for multivariate additive models," Journal of Multivariate Analysis, Elsevier, vol. 192(C).

4. 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. Zhilan Lou & Jun Shao & Menggang Yu, 2018. "Optimal treatment assignment to maximize expected outcome with multiple treatments," Biometrics, The International Biometric Society, vol. 74(2), pages 506-516, June.
   3. Abramovich, Felix & Pensky, Marianna, 2019. "Classification with many classes: Challenges and pluses," Journal of Multivariate Analysis, Elsevier, vol. 174(C).
   4. Mounia Hendel & Fethi Meghnefi & Mohamed El Amine Senoussaoui & Issouf Fofana & Mostefa Brahami, 2023. "Using Generic Direct M-SVM Model Improved by Kohonen Map and Dempster–Shafer Theory to Enhance Power Transformers Diagnostic," Sustainability, MDPI, vol. 15(21), pages 1-23, October.
   5. Engin Tas & Ayca Hatice Atli, 2024. "Stock Price Ranking by Learning Pairwise Preferences," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 513-528, February.
   6. Sarra Houidi & Dominique Fourer & François Auger & Houda Ben Attia Sethom & Laurence Miègeville, 2021. "Comparative Evaluation of Non-Intrusive Load Monitoring Methods Using Relevant Features and Transfer Learning," Energies, MDPI, vol. 14(9), pages 1-28, May.
   7. 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.
   8. 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.
   9. Yang, Yi & Guo, Yuxuan & Chang, Xiangyu, 2021. "Angle-based cost-sensitive multicategory classification," Computational Statistics & Data Analysis, Elsevier, vol. 156(C).
   10. 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.
   11. Xingye Qiao & Yufeng Liu, 2009. "Adaptive Weighted Learning for Unbalanced Multicategory Classification," Biometrics, The International Biometric Society, vol. 65(1), pages 159-168, March.
   12. 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.
   13. Víctor Blanco & Alberto Japón & Justo Puerto, 2020. "Optimal arrangements of hyperplanes for SVM-based multiclass classification," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(1), pages 175-199, March.
   14. Fan, Yiwei & Zhao, Junlong, 2022. "Safe sample screening rules for multicategory angle-based support vector machines," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).
   15. Park, Beomjin & Park, Changyi, 2021. "Kernel variable selection for multicategory support vector machines," Journal of Multivariate Analysis, Elsevier, vol. 186(C).
   16. 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.
   17. 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.
   18. Gardner-Lubbe, Sugnet, 2016. "A triplot for multiclass classification visualisation," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 20-32.
   19. Guangrui Tang & Neng Fan, 2022. "A Survey of Solution Path Algorithms for Regression and Classification Models," Annals of Data Science, Springer, vol. 9(4), pages 749-789, August.
   20. María Pérez-Ortiz & Silvia Jiménez-Fernández & Pedro A. Gutiérrez & Enrique Alexandre & César Hervás-Martínez & Sancho Salcedo-Sanz, 2016. "A Review of Classification Problems and Algorithms in Renewable Energy Applications," Energies, MDPI, vol. 9(8), pages 1-27, August.
   21. Qi Wang & Yue Ma & Kun Zhao & Yingjie Tian, 2022. "A Comprehensive Survey of Loss Functions in Machine Learning," Annals of Data Science, Springer, vol. 9(2), pages 187-212, April.
   22. 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.
   23. Maximilian Alber & Julian Zimmert & Urun Dogan & Marius Kloft, 2017. "Distributed optimization of multi-class SVMs," PLOS ONE, Public Library of Science, vol. 12(6), pages 1-18, June.
   24. Park, Beomjin & Park, Changyi, 2023. "Multiclass Laplacian support vector machine with functional analysis of variance decomposition," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
   25. Ozcan, Sercan & Suloglu, Metin & Sakar, C. Okan & Chatufale, Sushant, 2021. "Social media mining for ideation: Identification of sustainable solutions and opinions," Technovation, Elsevier, vol. 107(C).
   26. 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.
   27. Hossein Baloochian & Hamid Reza Ghaffary, 2019. "Multiclass Classification Based on Multi-criteria Decision-making," Journal of Classification, Springer;The Classification Society, vol. 36(1), pages 140-151, April.
   28. Safo, Sandra E. & Ahn, Jeongyoun, 2016. "General sparse multi-class linear discriminant analysis," Computational Statistics & Data Analysis, Elsevier, vol. 99(C), pages 81-90.
   29. Ali Anaissi & Madhu Goyal & Daniel R Catchpoole & Ali Braytee & Paul J Kennedy, 2016. "Ensemble Feature Learning of Genomic Data Using Support Vector Machine," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-17, June.
   30. 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.
   31. 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.
   32. Crystal T. Nguyen & Daniel J. Luckett & Anna R. Kahkoska & Grace E. Shearrer & Donna Spruijt‐Metz & Jaimie N. Davis & Michael R. Kosorok, 2020. "Estimating individualized treatment regimes from crossover designs," Biometrics, The International Biometric Society, vol. 76(3), pages 778-788, September.
   33. Pedro Duarte Silva, A., 2017. "Optimization approaches to Supervised Classification," European Journal of Operational Research, Elsevier, vol. 261(2), pages 772-788.
   34. 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.
   35. 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, September.
   36. 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.

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.

   Cited by:

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

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.

   Cited by:

   1. Didier Girard, 2010. "Estimating the accuracy of (local) cross-validation via randomised GCV choices in kernel or smoothing spline regression," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 22(1), pages 41-64.
   2. 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.
   3. Sung Wan Han & Rickson C. Mesquita & Theresa M. Busch & Mary E. Putt, 2014. "A method for choosing the smoothing parameter in a semi-parametric model for detecting change-points in blood flow," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(1), pages 26-45, January.

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.

   Cited by:

   1. Usset, Joseph & Staicu, Ana-Maria & Maity, Arnab, 2016. "Interaction models for functional regression," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 317-329.

8. 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.
   2. Mario Arturo Ruiz Estrada & Evangelos Koutronas & Ross Knippenberg, 2016. "The Mega Distributed Lag Model," Contemporary Economics, University of Economics and Human Sciences in Warsaw., vol. 10(2), June.

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