Challenges in Computational Statistics and Data Mining

This volume contains nineteen research papers belonging to theareas of computational statistics, data mining, and their applications. Those papers, all written specifically for this volume, are their authors contributions to honour and celebrate Professor Jacek Koronacki on the occcasion of his 70th birthday. Thebooks related and often interconnected topics, represent Jacek Koronackis research interests and their evolution. They also clearly indicate how close the areas of computational statistics and data mining are.

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[2]  Patrick D. Surry,et al.  Real-World Uplift Modelling with Significance-Based Uplift Trees , 2012 .

[3]  Gábor J. Székely,et al.  Hierarchical Clustering via Joint Between-Within Distances: Extending Ward's Minimum Variance Method , 2005, J. Classif..

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[7]  Eric Moulines,et al.  Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Machine Learning , 2011, NIPS.

[8]  Szymon Jaroszewicz,et al.  Decision trees for uplift modeling with single and multiple treatments , 2011, Knowledge and Information Systems.

[9]  S. Jaroszewicz,et al.  Uplift modeling for clinical trial data , 2012 .

[10]  G. Székely,et al.  TESTING FOR EQUAL DISTRIBUTIONS IN HIGH DIMENSION , 2004 .

[11]  Patrick D. Surry,et al.  Differential Response Analysis: Modeling True Responses by Isolating the Effect of a Single Action , 1999 .

[12]  Szymon Jaroszewicz,et al.  Decision Trees for Uplift Modeling , 2010, 2010 IEEE International Conference on Data Mining.

[13]  Leo Guelman,et al.  Random Forests for Uplift Modeling: An Insurance Customer Retention Case , 2012, MS.

[14]  Szymon Jaroszewicz,et al.  Ensemble methods for uplift modeling , 2014, Data Mining and Knowledge Discovery.

[15]  Stijn Vansteelandt,et al.  Causal inference with generalized structural mean models , 2003 .

[16]  William A. Knaus,et al.  The effectiveness of right heart catheterization in the initial care of critically ill patients. SUPPORT Investigators. , 1996, Journal of the American Medical Association (JAMA).

[17]  David Page,et al.  Support Vector Machines for Differential Prediction , 2014, ECML/PKDD.

[18]  Boris Polyak,et al.  Acceleration of stochastic approximation by averaging , 1992 .