Dimitris N. Politis is Professor of Mathematics and Adjunct Professor of Economics at the University of California, San Diego. His research interests include Time Series Analysis, Resampling and Subsampling, Nonparametric Function Estimation, and Model-free Prediction. He has served as Editor of the IMS Bulletin (2010-2013), Co-Editor of the Journal of Time Series Analysis (2013-present), Co-Editor of the Journal of Nonparametric Statistics (2008-2011), and as Associate Editor for several journals including Bernoulli, the Journal of the American Statistical Association, and the Journal of the Royal Statistical Society, Series B. He is a fellow of the Institute of Mathematical Statistics (IMS) and the American Statistical Association, former fellow of the John Simon Guggenheim Memorial Foundation, and co-founder (with M. Akritas and S.N. Lahiri) of the International Society for NonParametric Statistics.

“The monograph restores the emphasis on observable quantities. Considering model-free and model-based prediction, the monograph emphasises on model-free approach but also shows the close relation between these two approaches. The book is of interest for both academics and practitioners in the field of data analysis.” (Pavel Stoynov, zbMATH 1397.62008, 2018)“This self-contained and fascinating book, intended for advanced graduate and Ph.D. students, teachers, researchers and practitioners, is useful, well written, and directly oriented toward real data applications.” (Gilles Teyssière, Mathematical Reviews, February, 2017)

• Prediction: some heuristic notions.- The Model-free Prediction Principle.- Model-based prediction in regression.- Model-free prediction in regression.- Model-free vs. model-based confidence intervals.- Linear time series and optimal linear prediction.- Model-based prediction in autoregression.- Model-free inference for Markov processes.- Predictive inference for locally stationary time series.- Model-free vs. model-based volatility prediction.

Dimitris N. Politis is Professor of Mathematics and Adjunct Professor of Economics at the University of California, San Diego. His research interests include Time Series Analysis, Resampling and Subsampling, Nonparametric Function Estimation, and Model-free Prediction. He has served as Editor of the IMS Bulletin (2010-2013), Co-Editor of the Journal of Time Series Analysis (2013-present), Co-Editor of the Journal of Nonparametric Statistics (2008-2011), and as Associate Editor for several journals including Bernoulli, the Journal of the American Statistical Association, and the Journal of the Royal Statistical Society, Series B. He is a fellow of the Institute of Mathematical Statistics (IMS) and the American Statistical Association, former fellow of the John Simon Guggenheim Memorial Foundation, and co-founder (with M. Akritas and S.N. Lahiri) of the International Society for NonParametric Statistics.

“The monograph restores the emphasis on observable quantities. Considering model-free and model-based prediction, the monograph emphasises on model-free approach but also shows the close relation between these two approaches. The book is of interest for both academics and practitioners in the field of data analysis.” (Pavel Stoynov, zbMATH 1397.62008, 2018)“This self-contained and fascinating book, intended for advanced graduate and Ph.D. students, teachers, researchers and practitioners, is useful, well written, and directly oriented toward real data applications.” (Gilles Teyssière, Mathematical Reviews, February, 2017)

• Prediction: some heuristic notions.- The Model-free Prediction Principle.- Model-based prediction in regression.- Model-free prediction in regression.- Model-free vs. model-based confidence intervals.- Linear time series and optimal linear prediction.- Model-based prediction in autoregression.- Model-free inference for Markov processes.- Predictive inference for locally stationary time series.- Model-free vs. model-based volatility prediction.

Dimitris N. Politis is Professor of Mathematics and Adjunct Professor of Economics at the University of California, San Diego. His research interests include Time Series Analysis, Resampling and Subsampling, Nonparametric Function Estimation, and Model-free Prediction. He has served as Editor of the IMS Bulletin (2010-2013), Co-Editor of the Journal of Time Series Analysis (2013-present), Co-Editor of the Journal of Nonparametric Statistics (2008-2011), and as Associate Editor for several journals including Bernoulli, the Journal of the American Statistical Association, and the Journal of the Royal Statistical Society, Series B. He is a fellow of the Institute of Mathematical Statistics (IMS) and the American Statistical Association, former fellow of the John Simon Guggenheim Memorial Foundation, and co-founder (with M. Akritas and S.N. Lahiri) of the International Society for NonParametric Statistics.

“The monograph restores the emphasis on observable quantities. Considering model-free and model-based prediction, the monograph emphasises on model-free approach but also shows the close relation between these two approaches. The book is of interest for both academics and practitioners in the field of data analysis.” (Pavel Stoynov, zbMATH 1397.62008, 2018)“This self-contained and fascinating book, intended for advanced graduate and Ph.D. students, teachers, researchers and practitioners, is useful, well written, and directly oriented toward real data applications.” (Gilles Teyssière, Mathematical Reviews, February, 2017)

• Prediction: some heuristic notions.- The Model-free Prediction Principle.- Model-based prediction in regression.- Model-free prediction in regression.- Model-free vs. model-based confidence intervals.- Linear time series and optimal linear prediction.- Model-based prediction in autoregression.- Model-free inference for Markov processes.- Predictive inference for locally stationary time series.- Model-free vs. model-based volatility prediction.