Results 221 to 230 of about 43,321 (262)

Variable selection using P‐splines

WIREs Computational Statistics, 2014
Selecting among a large set of variables those that influence most a response variable is an important problem in statistics. When the assumed regression model involves a nonparametric component, penalized regression techniques, and in particular P‐splines, are among the commonly used methods.
Gijbels, Irène, Verhasselt, Anneleen, Vrinssen, Inge   +2 more
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Flexible smoothing with P-splines: a unified approach

Statistical Modelling, 2002
We consider the application of P-splines (Eilers and Marx, 1996) to three classes of models with smooth components: semiparametric models, models with serially correlated errors, and models with heteroscedastic errors. We show that P-splines provide a common approach to these problems. We set out a simple nonparametric strategy for the choice of the P-
Currie, I. D., Durban, M.
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Semiparametric transformation models with Bayesian P-splines

Statistics and Computing, 2011
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Xinyuan Song 0001, Zhaohua Lu
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Simple and multiple P‐splines regression with shape constraints

British Journal of Mathematical and Statistical Psychology, 2006
In many research areas, especially within social and behavioural sciences, the relationship between predictor and criterion variables is often assumed to have a particular shape, such as monotone, single‐peaked or U‐shaped. Such assumptions can be transformed into (local or global) constraints on the sign of the
Bollaerts, K., Eilers, P.H.C., Mechelen, van, I.   +2 more
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Parsimonious time series clustering using P-splines

Expert Systems with Applications, 2016
A new parsimonious way to cluster time (data) series is provided.We deal with P-spline framework and non-hierarchical clustering.Simulation studies and two well-known real world case studies are performed. We introduce a parsimonious model-based framework for clustering time course data.
Carmela Iorio, Gianluca Frasso, Antonio D'Ambrosio, Roberta Siciliano   +3 more
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Variable Selection in Additive Models Using P-Splines

Technometrics, 2012
This article extends the nonnegative garrote method to a component selection method in a nonparametric additive model in which each univariate function is estimated with P-splines. We also establish the consistency of the procedure. An advantage of P-splines is that the fitted function is represented in a rather small basis of B-splines.
Anestis Antoniadis, Irène Gijbels, Anneleen Verhasselt   +2 more
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Sharpening P-spline signal regression

Statistical Modelling, 2008
We propose two variations of P-spline signal regression: space-varying penalization signal regression (SPSR) and additive polynomial signal regression (APSR). SPSR uses space-varying roughness penalty according to the estimated coefficients from the partial least-squares (PLS) regression, while APSR expands the linear basis to polynomial bases.
Li, Bin, Marx, Brian D.
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A note on P-spline additive models with correlated errors

Computational Statistics, 2003
The model under consideration is an additive model where the response variable \(y\) is represented as a sum of \(k\) smooth terms which act on the explanatory variables \(x_1,\dots,x_k\): \[ y=\alpha+ f_1(x_1)+\dots+f_k(x_k)+\varepsilon, \qquad E(f_i(x_i))=0, \] in the presence of correlated Gaussian errors.
María Durbán, Iain D. Currie
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P-splines regression smoothing and difference type of penalty

Statistics and Computing, 2009
P-splines regression provides a flexible smoothing tool. In this paper we consider difference type penalties in a context of nonparametric generalized linear models, and investigate the impact of the order of the differencing operator. Minimizing Akaike's information criterion we search for a possible best data-driven value of the differencing order ...
Irène Gijbels, Anneleen Verhasselt
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A P-spline based clustering approach for portfolio selection

Expert Systems with Applications, 2018
Abstract In the last years, many clustering techniques dealing with time course data have been proposed due to recent interests in studying phenomena that change over time. A new clustering method suitable for time series applications has been recently proposed by exploiting the properties of the P-splines approach.
Carmela Iorio, Gianluca Frasso, Antonio D'Ambrosio, Roberta Siciliano   +3 more
openaire   +2 more sources