In this volume, we study univariate nonparametric regression problems. The prototypical example is where one observes the data where ε 1, ε 2, …, ε n are independent normal random variables with mean 0 and unknown variance σ2. The object is to estimate the (smooth) function f o and construct inferential procedures regarding the model (1.2). However, the real purpose of this volume is to outline a down-to-earth approach to nonparametric regression problems that may be emulated in other settings. Here, in the introductory chapter, we loosely survey what we will be doing and which standard topics will be omitted. However, before doing that, it is worthwhile to describe various problems in which the need for nonparametric regression arises.
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© 2009 Springer-Verlag New York
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Eggermont, P.P.B., LaRiccia, V.N. (2009). Nonparametric Regression. In: Maximum Penalized Likelihood Estimation. Springer Series in Statistics. Springer, New York, NY. https://doi.org/10.1007/b12285_1
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DOI: https://doi.org/10.1007/b12285_1
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