Grace Wahba9780898712445, 0898712440
Convergence properties, data based smoothing parameter selection, confidence intervals, and numerical methods are established which are appropriate to a wide variety of problems which fall within this framework. Methods for including side conditions and other prior information in solving ill-posed inverse problems are included. Data which involves samples of random variables with Gaussian, Poisson, binomial, and other distributions are treated in a unified optimization context. Experimental design questions, i.e., which functionals should be observed, are studied in a general context. Extensions to distributed parameter system identification problems are made by considering implicitly defined functionals.
Table of contents :
Spline Models for Observational Data……Page 1
Contents……Page 10
Foreword……Page 12
CHAPTER 1 Background……Page 18
CHAPTER 2 More Splines……Page 38
CHAPTER 3 Equivalence and Perpendicularity, or,What’s So Special About Splines?……Page 58
CHAPTER 4 Estimating the Smoothing Parameter……Page 62
CHAPTER 5 “Confidence Intervals”……Page 84
CHAPTER 6 Partial Spline Models……Page 90
CHAPTER 7 Finite-Dimensional Approximating Subspaces……Page 112
CHAPTER 8 Fredholm Integral Equations of the First Kind……Page 118
CHAPTER 9 Further Nonlinear Generalizations……Page 126
CHAPTER 10 Additive and Interaction Splines……Page 144
CHAPTER 1 1 Numerical Methods……Page 152
CHAPTER 12 Special Topics……Page 162
Bibliography……Page 170
Author Index……Page 184
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