Search results for: introduction-to-nonparametric-estimation

Introduction to Nonparametric Estimation

Author : Alexandre B. Tsybakov
File Size : 38.50 MB
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Developed from lecture notes and ready to be used for a course on the graduate level, this concise text aims to introduce the fundamental concepts of nonparametric estimation theory while maintaining the exposition suitable for a first approach in the field.

An Introduction to Nonparametric Statistics

Author : John E. Kolassa
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An Introduction to Nonparametric Statistics presents techniques for statistical analysis in the absence of strong assumptions about the distributions generating the data. Rank-based and resampling techniques are heavily represented, but robust techniques are considered as well. These techniques include one-sample testing and estimation, multi-sample testing and estimation, and regression. Attention is paid to the intellectual development of the field, with a thorough review of bibliographical references. Computational tools, in R and SAS, are developed and illustrated via examples. Exercises designed to reinforce examples are included. Features Rank-based techniques including sign, Kruskal-Wallis, Friedman, Mann-Whitney and Wilcoxon tests are presented Tests are inverted to produce estimates and confidence intervals Multivariate tests are explored Techniques reflecting the dependence of a response variable on explanatory variables are presented Density estimation is explored The bootstrap and jackknife are discussed This text is intended for a graduate student in applied statistics. The course is best taken after an introductory course in statistical methodology, elementary probability, and regression. Mathematical prerequisites include calculus through multivariate differentiation and integration, and, ideally, a course in matrix algebra.

Nonparametric Curve Estimation

Author : Sam Efromovich
File Size : 70.23 MB
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This book gives a systematic, comprehensive, and unified account of modern nonparametric statistics of density estimation, nonparametric regression, filtering signals, and time series analysis. The companion software package, available over the Internet, brings all of the discussed topics into the realm of interactive research. Virtually every claim and development mentioned in the book is illustrated with graphs which are available for the reader to reproduce and modify, making the material fully transparent and allowing for complete interactivity.

Nonparametric Econometrics

Author : Adrian Pagan
File Size : 86.6 MB
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Covering the vast literature on the nonparametric and semiparametric statistics and econometrics that has evolved over the last five decades, this book will be useful for first year graduate courses in econometrics.

Nonparametric Estimation under Shape Constraints

Author : Piet Groeneboom
File Size : 70.78 MB
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This book introduces basic concepts of shape constrained inference and guides the reader to current developments in the subject.

Introductory Statistics

Author : Thomas H. Wonnacott
File Size : 62.77 MB
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Descriptive statistics for samples; Probability; Random variables and their distributions; Two random variables; Sampling; Estimation; Hypothesis testing; Analysis of variance; Introduction to regression; Regression theory; Multiple regression; Correlation; Bayesian decision theory; Nonparametric statistics; Chi square tests; Maximum likelihood estimation.

Advances in Soviet Mathematics

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File Size : 84.88 MB
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Nonparametric Estimation Subject to Shape Restrictions

Author : Yazhen Wang
File Size : 35.13 MB
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Consistent Nonparametric Estimation of Best Linear Classification Rules

Author : Richard Lee Greer
File Size : 46.75 MB
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An Experimental Design for Nonparametric Estimation of Correlation Under Destructive Testing

Author : Kuo-Tsung Wu
File Size : 69.91 MB
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Nonparametric Estimation of Mortality from Cohorts of Lifetables

Author : William B. Capra
File Size : 40.13 MB
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Automation and Remote Control

Author :
File Size : 75.67 MB
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Nonparametric Estimation of Weights in Least squares Regression Analysis

Author : Robin Lawrence Rose
File Size : 48.89 MB
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Nonparametric Estimation and Model Selection Using Constrained Splines in Linear Inversion Problems

Author : Davide Verotta
File Size : 34.42 MB
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On Nonparametric Estimation and Inference with Censored Data Bandwidth Selection for Local Polynomial Regression and Subset Selection in Explanatory Regression Analyses

Author : Derick Randall Peterson
File Size : 33.71 MB
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Constrained Nonparametric Estimation Via Mixtures

Author : Peter David Hoff
File Size : 55.73 MB
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Missing and Modified Data in Nonparametric Estimation

Author : Sam Efromovich
File Size : 55.35 MB
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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.

Nonparametric Estimation

Author : Constance van Eeden
File Size : 57.4 MB
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Nonparametric Functional Data Analysis

Author : Frédéric Ferraty
File Size : 24.92 MB
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Modern apparatuses allow us to collect samples of functional data, mainly curves but also images. On the other hand, nonparametric statistics produces useful tools for standard data exploration. This book links these two fields of modern statistics by explaining how functional data can be studied through parameter-free statistical ideas. At the same time it shows how functional data can be studied through parameter-free statistical ideas, and offers an original presentation of new nonparametric statistical methods for functional data analysis.

Nonparametric and Parametric Estimation with Truncated Regression Data

Author : Kwok-Leung Tsui
File Size : 33.77 MB
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