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ON SEMIPARAMETRIC REGRESSION WITH O'SULLIVAN PENALIZED SPLINES [PDF]
Australian & New Zealand Journal of Statistics, 2008 SummaryAn exposition on the use of O'Sullivan penalized splines in contemporary semiparametric regression, including mixed model and Bayesian formulations, is presented. O'Sullivan penalized splines are similar to P‐splines, but have the advantage of being a direct generalization of smoothing splines. Exact expressions for the O'Sullivan penalty matrix
Wand, M., Ormerod, J. T.
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Application of Penalized Splines in Analyzing Neuronal Data [PDF]
Biometrical Journal, 2009 AbstractNeuron experiments produce high‐dimensional data structures. Therefore, application of smoothing techniques in the analysis of neuronal data from electrophysiological experiments has received considerable attention of late. We investigate the use of penalized splines in the analysis of neuronal data. This is first illustrated when interested in
MARINGWA, John +7 more
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Direct Determination of Smoothing Parameter for Penalized Spline Regression
Journal of Probability and Statistics, 2014 Penalized spline estimator is one of the useful smoothing methods. To construct the estimator, having goodness of fit and smoothness, the smoothing parameter should be appropriately selected. The purpose of this paper is to select the smoothing parameter
Takuma Yoshida
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Intensity estimation on geometric networks with penalized splines
The Annals of Applied Statistics, 2022 In the past decades, the growing amount of network data has lead to many novel statistical models. In this paper we consider so called geometric networks. Typical examples are road networks or other infrastructure networks. But also the neurons or the blood vessels in a human body can be interpreted as a geometric network embedded in a three ...
Schneble, Marc, Kauermann, Göran
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Spline Semiparametric Regression Models
Journal of Kufa for Mathematics and Computer, 2015 In this paper, we study semiparametric regression models with spline smoothing, and determining the numbers of knots and their locations by using some statistical criteria, a simulation model has been performed.
Ameera Jaber Mohaisen +1 more
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The use of spline, Bayesian spline and penalized Bayesian spline regression for modeling
, 2021 Splayn, cezalandırılmış splayn ve Bayesyen splayn olarak adlandırılan parametrik olmayan regresyon yöntemleri modellemede esneklik ve sabit bir modele bağlı olmamak gibi büyük avantajlar sağlar. Cezalandırılmış splayn regresyon, parametrik olmayan splayn düzeltme düşüncesini kullanır.
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On the asymptotics of penalized spline smoothing
Electronic Journal of Statistics, 2011 This paper performs an asymptotic analysis of penalized spline estimators. We compare P-splines and splines with a penalty of the type used with smoothing splines. The asymptotic rates of the supremum norm of the difference between these two estimators over compact subsets of the interior and over the entire interval are established. It is shown that a
Wang, Xiao +2 more
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Smoothness Selection for Penalized Quantile Regression Splines [PDF]
The International Journal of Biostatistics, 2012 Modern data-rich analyses may call for fitting a large number of nonparametric quantile regressions. For example, growth charts may be constructed for each of a collection of variables, to identify those for which individuals with a disorder tend to fall in the tails of their age-specific distribution; such variables might serve as developmental ...
Philip T, Reiss, Lei, Huang
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Clinical Epidemiology and Global Health, 2022 Statistical modelling is pivotal in assessing intensity of a stochastic processes. Novel Corona virus disease demanded proactive measures to understand the severity of disease spread and to plan its control accordingly.
Ranjita Pandey, Himanshu Tolani
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A note on bimodality in the log-likelihood function for penalized spline mixed models
, 2009 For a smoothing spline or general penalized spline model, the smoothing parameter can be estimated using residual maximum likelihood (REML) methods by expressing the spline in the form of a mixed model.
Welham, S. J., Thompson, R.
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