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Introduction

  • Chong Gu
Part of the Springer Series in Statistics book series (SSS)

Abstract

Data and models are two sources of information in a statistical analysis. Data carry noise but are “unbiased,” whereas models, effectively a set of constraints, help to reduce noise but are responsible for “biases.” Representing the two extremes on the spectrum of “bias-variance” trade-off are standard parametric models and constraint-free nonparametric “models” such as the empirical distribution for a probability density. In between the two extremes, there exist scores of nonparametric or semiparametric models, of which most are also known as smoothing methods. A family of such nonparametric models in a variety of stochastic settings can be derived through the penalized likelihood method, forming the subject of this book.

Keywords

Smoothing Parameter Side Condition Smoothing Spline Multivariate Function Multivariate Statistical Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 2002

Authors and Affiliations

  • Chong Gu
    • 1
  1. 1.Department of StatisticsPurdue UniversityWest LafayetteUSA

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