Results 261 to 270 of about 43,843 (275)

Pharmacokinetic parameters estimation using adaptive Bayesian P‐splines models

Pharmaceutical Statistics, 2008
AbstractIn preclinical and clinical experiments, pharmacokinetic (PK) studies are designed to analyse the evolution of drug concentration in plasma over time i.e. the PK profile. Some PK parameters are estimated in order to summarize the complete drug's kinetic profile: area under the curve (AUC), maximal concentration (Cmax), time at which the maximal
Jullion, Astrid, Lambert, Philippe, Beck, Benoit, Vandenhende, François   +3 more
openaire   +2 more sources

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
openaire   +3 more sources

Semiparametric transformation models with Bayesian P-splines

Statistics and Computing, 2011
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Song, Xin-yuan, Lu, Zhao-hua
openaire   +2 more sources

Frequency Analysis of Recurrence Variational P-Splines

Optoelectronics, Instrumentation and Data Processing, 2017
Frequency responses of the procedure of spline-smoothing of information coming in real time are obtained. A recurrence spline is studied from the standpoint of the theory of linear dynamical systems. The estimation of quality and sustainability of the recurrence spline filter are described.
E. A. Kochegurova, A. I. Kochegurov, N. E. Rozhkova   +2 more
openaire   +1 more source

Generalized Linear Models with P-splines

1992
B-splines with equidistant knots are attractive for nonparametric models, but allow only limited control over smoothness. We propose a simple but effective remedy: use relatively many knots but put a difference penalty on the coefficients of the B-splines.
Paul H. C. Eilers, Brian D. Marx
openaire   +1 more source

P-spline curves

The Visual Computer, 2022
Zhihao Wang, Yajuan Li, Huixia Xu, Jianzhen Liu, Chongyang Deng   +4 more
openaire   +1 more source

Variable Selection in Varying-Coefficient Models Using P-Splines

Journal of Computational and Graphical Statistics, 2012
In this article, we consider nonparametric smoothing and variable selection in varying-coefficient models. Varying-coefficient models are commonly used for analyzing the time-dependent effects of covariates on responses measured repeatedly (such as longitudinal data). We present the P-spline estimator in this context and show its estimation consistency
Antoniadis, Anestis, Gijbels, Irène, Verhasselt, Anneleen   +2 more
openaire   +3 more sources

Regularisation and P-splines in generalised linear models

Journal of Nonparametric Statistics, 2010
P-splines regression is a flexible smoothing tool in which the starting point is a highly parameterised model and overfitting is prevented by introducing a penalty function. A common form of the penalty term is obtained by taking a prespecified order of differences of adjacent coefficients. This paper deals with a data-driven choice of the differencing
Irène Gijbels, Anneleen Verhasselt
openaire   +1 more source

Spatially Adaptive Bayesian Penalized Regression Splines (P-splines)

Journal of Computational and Graphical Statistics, 2005
In this article we study penalized regression splines (P-splines), which are low-order basis splines with a penalty to avoid undersmoothing. Such P-splines are typically not spatially adaptive, and hence can have trouble when functions are varying rapidly.
Veerabhadran Baladandayuthapani, Bani K Mallick, Raymond J Carroll   +2 more
openaire   +1 more source

Semiparametric Latent Variable Models With Bayesian P-Splines

Journal of Computational and Graphical Statistics, 2010
This article aims to develop a semiparametric latent variable model, in which outcome latent variables are related to explanatory latent variables and covariates through an additive structural equation formulated by a series of unspecified smooth functions. The Bayesian P-splines approach, together with a Markov chain Monte Carlo algorithm, is proposed
Xin-Yuan Song, Zhao-Hua Lu
openaire   +1 more source