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Adaptive Priors based on Splines with Random Knots
Adaptive estimation bayesian non-parametric optimal contrac-tion rate spline random knots
2013/4/27
Splines are useful building blocks when constructing priors on nonparametric models indexed by functions. Recently it has been established in the literature that hierarchical priors based on splines w...
Asymptotics for penalized splines in generalized additive models
Asymptotic normality B-spline Generalized additive model Mixed model Pe-nalized spline
2012/9/17
This paper discusses asymptotic theory for penalized spline estimators in generalized additive models. The purpose of this paper is to establish the asymptotic bias and variance as well as the asympto...
Bayesian semi-parametric forecasting with penalised splines and autoregressive errors
splines, autoregressive errors, semi-parametric regression, Bayesian
2012/9/18
Observational time series data often exhibit both cyclic temporal trends and autocorrelation and may also depend on covariates. As such, there is a need for exible regression models that are able to c...
Group-Lasso on Splines for Spectrum Cartography
Sparsity, splines (group-)Lasso field estimation cognitive radio sensing
2010/10/14
The unceasing demand for continuous situational awareness calls for innovative and large-scale signal processing algorithms, complemented by collaborative and adaptive sensing platforms to accomplish...
Free-knot Splines and Adaptive Knot Selection
adaptive model selection evolutionary algorithms inhomogeneous smoothness non-parametric regression signal processing spatial adaptation variable multiple knots
2009/3/9
Conventional spline procedures have proven to be effective and useful for estimating smooth functions. However, these procedures find piecewise and inhomogeneous smooth functions difficult to handle. ...
Smoothing splines estimators for functional linear regression
Functional linear regression functional parameter functionalvariable smoothing splines
2010/3/18
The paper considers functional linear regression, where scalar responses
Y1, . . . ,Yn are modeled in dependence of random functions
X1, . . . ,Xn. We propose a smoothing splines estimator for the f...
On semiparametric regression with O'Sullivan penalised splines
Additive models Markov chain Monte Carlo Mixed models P-splines Smoothing splines
2010/4/30
This is an expos´e on the use of O’Sullivan penalised splines in contemporary semiparametric
regression, including mixed model and Bayesian formulations. O’Sullivan penalised
splines are simil...
