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Donor factors influencing pancreas transplant utilization decisions; using US registry data to model evolution in decision-making over time

Patel C, Kourounis G, Leeuwen Lv, Holzner M, Wadhera V, Akhtar MZ, Florman S, Maillo-Nieto A, Shaw J, White S, Wilson C, Tingle S.   +11 more
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Laplacian-P-Splines in Gamma Frailty Survival Model

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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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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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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.
IORIO, CARMELA, Frasso, Gianluca, D'AMBROSIO, ANTONIO, SICILIANO, ROBERTA   +3 more
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Image interpolation using adaptive P-spline

2015 Annual IEEE India Conference (INDICON), 2015
This paper introduces a novel scheme for image interpolation. The basic contribution of the proposed interpolation scheme lies in two aspects. Firstly, the missing pixels in the image to be interpolated are classified into smooth and non-smooth based on the local characteristic of the image. Secondly regularized version of B-spline i.e.
Rajashree Nayak, Dipti Patra
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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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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.
Antoniadis, Anestis, Gijbels, Irène, Verhasselt, Anneleen   +2 more
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