Results 61 to 70 of about 170,593 (200)

Using penalized spline, generalized additive model and mixed model regression techniques to examine univariate and multivariate time series and in particular business cycles [PDF]

, 2013
Teuber T. Using penalized spline, generalized additive model and mixed model regression techniques to examine univariate and multivariate time series and in particular business cycles.
Teuber, Timo
core  

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, Ammar Muslim Abdulhussein   +1 more
doaj   +1 more source

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
doaj   +1 more source

A note on smoothing parameter selection for penalized spline smoothing

, 2005
Kauermann G. A note on smoothing parameter selection for penalized spline smoothing. JOURNAL OF STATISTICAL PLANNING AND INFERENCE. 2005;127(1-2):53-69.In nonparametric regression the smoothing parameter can be selected by minimizing a Mean Squared Error
Kauermann, Göran
core   +1 more source

Automatic spline-smoothing approach applied to denoise Moroccan resistivity data phosphate deposit “disturbances” map

Ingeniería e Investigación, 2008
This paper focuses on presenting a method which is able to filter out noise and suppress outliers of sampled real functions under fairly general conditions.
Saad Bakkali, Lahcen Bahi
doaj  

Mixed interpolating–smoothing splines and the ν-spline

Journal of Mathematical Analysis and Applications, 2006
Let \(X\), \(Y\), \(Z_{1}\) and \(Z_{2}\) be Hilbert spaces with inner products \(\langle\;,\;\rangle_{X}\), \(\langle\;,\;\rangle_{Y}\), \(\langle\;,\;\rangle_{Z_{1}}\) and \(\langle\;,\;\rangle_{Z_{2}}\) respectively, and let \(T:X\rightarrow Y\), \(\Lambda _{1}:X\rightarrow Z_{1}\), \(\Lambda _{2}:X\rightarrow Z_{2}\) and \(\pi :Z_{2}\rightarrow Z_ ...
openaire   +2 more sources

Local Linear Forecasts Using Cubic Smoothing Splines [PDF]

We show how cubic smoothing splines fitted to univariate time series data can be used to obtain local linear forecasts. Our approach is based on a stochastic state space model which allows the use of a likelihood approach for estimating the smoothing ...
Baki Billah, Maxwell L. King, Ivet Pitrun, Rob J Hyndman   +3 more
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Smoothing: Local Regression Techniques [PDF]

Smoothing methods attempt to find functional relationships between different measurements. As in the standard regression setting, the data is assumed to consist of measurements of a response variable, and one or more predictor variables.
Loader, Catherine
core  

Differentiation of matrix functionals using triangular factorization [PDF]

, 2011
In various applications, it is necessary to differentiate a matrix functional w(A(x)), where A(x) is a matrix depending on a parameter vector x. Usually, the functional itself can be readily computed from a triangular factorization of A(x).
Anderssen, R.S., de Hoog, F.R., Lukas, M.A.   +2 more
core  

The Estimating Parameter and Number of Knots for Nonparametric Regression Methods in Modelling Time Series Data

Modelling
This research aims to explore and compare several nonparametric regression techniques, including smoothing splines, natural cubic splines, B-splines, and penalized spline methods.
Autcha Araveeporn
doaj   +1 more source