Akritas, Michael G., Lahiri, S. N., Politis, Dimitris N. (Eds.)
2014, XVI, 367 p. 64 illus., 24 illus. in color.
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Based on the influential first conference of the International Society for Non Parametric Statistics, which has an Advisory Committee featuring renowned researchers from around the world
Provides important developments in statistical computing, high-dimensional data, machine learning, data mining and resampling methods
Intended for advanced students and researchers
This volume is composed of peer-reviewed papers that have developed from the First Conference of the International Society for NonParametric Statistics (ISNPS). This inaugural conference took place in Chalkidiki, Greece, June 15-19, 2012. It was organized with the co-sponsorship of the IMS, the ISI, and other organizations. M.G. Akritas, S.N. Lahiri, and D.N. Politis are the first executive committee members of ISNPS, and the editors of this volume. ISNPS has a distinguished Advisory Committee that includes Professors R.Beran, P.Bickel, R. Carroll, D. Cook, P. Hall, R. Johnson, B. Lindsay, E. Parzen, P. Robinson, M. Rosenblatt, G. Roussas, T. SubbaRao, and G. Wahba. The Charting Committee of ISNPS consists of more than 50 prominent researchers from all over the world.
The chapters in this volume bring forth recent advances and trends in several areas of nonparametric statistics. In this way, the volume facilitates the exchange of research ideas, promotes collaboration among researchers from all over the world, and contributes to the further development of the field. The conference program included over 250 talks, including special invited talks, plenary talks, and contributed talks on all areas of nonparametric statistics. Out of these talks, some of the most pertinent ones have been refereed and developed into chapters that share both research and developments in the field.
Content Level »Research
Keywords »Function estimation - Functional data - High dimensional data - Nonparametric regression - Nonparametric statistics - Resampling methods - Statistical learning