Nonparametric methods

This book presents a systematic and unified approach for modern nonparametric treatment of missing and modified data via examples of density and hazard rate estimation, nonparametric regression, filtering signals, and time series analysis. All basic types of missing at random and not at random, biasing, truncation, censoring, and measurement errors are discussed, and their treatment is explained. Ten chapters of the book cover basic cases of direct data, biased data, nondestructive and destructive missing, survival data modified by truncation and censoring, missing survival data, stationary and nonstationary time series and processes, and ill-posed modifications. The coverage is suitable for self-study or a one-semester course for graduate students with a prerequisite of a standard course in introductory probability. Exercises of various levels of difficulty will be helpful for the instructor and self-study. The book is primarily about practically important small samples. It explains when consistent estimation is possible, and why in some cases missing data should be ignored and why others must be considered. If missing or data modification makes consistent estimation impossible, then the author explains what type of action is needed to restore the lost information. The book contains more than a hundred figures with simulated data that explain virtually every setting, claim, and development. The companion R software package allows the reader to verify, reproduce and modify every simulation and used estimators. This makes the material fully transparent and allows one to study it interactively. Sam Efromovich is the Endowed Professor of Mathematical Sciences and the Head of the Actuarial Program at the University of Texas at Dallas. He is well known for his work on the theory and application of nonparametric curve estimation and is the author of Nonparametric Curve Estimation: Methods, Theory, and Applications. Professor Sam Efromovich is a Fellow of the Institute of Mathematical Statistics and the American Statistical Association.

The theory and methods of smoothing have been developed mainly in the last ten years. The intensive interest in smoothing over this last decade had two reasons: statisticians realized that pure parametric thinking in curve estimations often does not meet the need forexibility in data analysis and the development of hardware created the demand for theoryof now computable nonparametric estimates.

多元数据的非参数多变化点检测 在这个项目中，我从下面的出色非参数变化点检测论文中提供了除法算法的python实现。 Matteson, David S., and Nicholas A. James. "A nonparametric approach for multiple change point analysis of multivariate data." Journal of the American Statistical Association 109.505 (2014): 334-345. 该论文的作者提供了一个R包，其中包含本文中讨论的其他算法 我还提供了一个Jupyter笔记本，用于评估综合数据集上的算法。

by Peter Müller, Fernando Andres Quintana, et al. | Jun 18, 2015

本书旨在介绍适用于平滑技术的统计和数学原理。

Introduction to Nonparametric Estimation 英文版

All of nonparametric statistics 非参数贝叶斯统计的首部专著啊 很好的书

实现的方法是： Lee、Suzanne S. 和 Per A. Mykland。 “金融市场的跳跃：新的非参数检验和跳跃动力学。” 金融研究评论 21.6 (2007): 2535-2563。

这是关于非线性时间序列非参数与参数模型的电子书，高清，最新版本，经典著作，英文版