Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/90457
Authors: 
Krivobokova, Tatyana
Year of Publication: 
2011
Series/Report no.: 
Courant Research Centre: Poverty, Equity and Growth - Discussion Papers 85
Abstract: 
There are two popular smoothing parameter selection methods for spline smoothing. First, criteria that approximate the average mean squared error of the estimator (e.g. generalized cross validation) are widely used. Alternatively, the maximum likelihood paradigm can be employed under the assumption that the underlying function to be estimated is a realization of some stochastic process. In this article the asymptotic properties of both smoothing parameter estimators are studied and compared in the frequentist and stochastic framework for penalized spline smoothing. Consistency and asymptotic normality of the estimators are proved and small sample properties are discussed. A simulation study and a real data example illustrate the theoretical fi ndings.
Subjects: 
Maximum likelihood
Mean squared error minimizer
Penalized splines
Smoothing splines
Document Type: 
Working Paper

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