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Smoothing Methods in Statistics

  • Book
  • © 1996

Overview

Authors:
  1. Jeffrey S. Simonoff

    1. Department of Statistics and Operations Research, Leonard N. Stern School of Business, New York University, New York, USA

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Part of the book series: Springer Series in Statistics (SSS)

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Table of contents (7 chapters)

  1. Front Matter

    Pages i-xii
    Download chapter PDF

  2. Introduction

    • Jeffrey S. Simonoff
    Pages 1-12

  3. Simple Univariate Density Estimation

    • Jeffrey S. Simonoff
    Pages 13-39

  4. Smoother Univariate Density Estimation

    • Jeffrey S. Simonoff
    Pages 40-95

  5. Multivariate Density Estimation

    • Jeffrey S. Simonoff
    Pages 96-133

  6. Nonparametric Regression

    • Jeffrey S. Simonoff
    Pages 134-214

  7. Smoothing Ordered Categorical Data

    • Jeffrey S. Simonoff
    Pages 215-251

  8. Further Applications of Smoothing

    • Jeffrey S. Simonoff
    Pages 252-274

  9. Back Matter

    Pages 275-339
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Keywords

About this book

The existence of high speed, inexpensive computing has made it easy to look at data in ways that were once impossible. Where once a data analyst was forced to make restrictive assumptions before beginning, the power of the computer now allows great freedom in deciding where an analysis should go. One area that has benefited greatly from this new freedom is that of non parametric density, distribution, and regression function estimation, or what are generally called smoothing methods. Most people are familiar with some smoothing methods (such as the histogram) but are unlikely to know about more recent developments that could be useful to them. If a group of experts on statistical smoothing methods are put in a room, two things are likely to happen. First, they will agree that data analysts seriously underappreciate smoothing methods. Smoothing meth­ ods use computing power to give analysts the ability to highlight unusual structure very effectively, by taking advantage of people's abilities to draw conclusions from well-designed graphics. Data analysts should take advan­ tage of this, they will argue.

Reviews

"...an excellent reference to the field and has no rival in terms of accessibility, coverage, and utility." (Jnl. of the Am. Statistical Association)
"...an excellent overview of smoothing methods and concepts, presenting material in an intuitive manner with many interesting graphics." (Technometrics)

Authors and Affiliations

  • Department of Statistics and Operations Research, Leonard N. Stern School of Business, New York University, New York, USA

    Jeffrey S. Simonoff

Bibliographic Information

  • Book Title: Smoothing Methods in Statistics

  • Authors: Jeffrey S. Simonoff

  • Series Title: Springer Series in Statistics

  • DOI: https://doi.org/10.1007/978-1-4612-4026-6

  • Publisher: Springer New York, NY

  • eBook Packages: Springer Book Archive

  • Copyright Information: Springer-Verlag New York, Inc. 1996

  • Hardcover ISBN: 978-0-387-94716-7Published: 06 June 1996

  • Softcover ISBN: 978-1-4612-8472-7Published: 16 September 2011

  • eBook ISBN: 978-1-4612-4026-6Published: 06 December 2012

  • Series ISSN: 0172-7397

  • Series E-ISSN: 2197-568X

  • Edition Number: 1

  • Number of Pages: XII, 340

  • Topics: Probability Theory and Stochastic Processes

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