Results 31 to 40 of about 1,351,383 (295)
A Spline Smoothing Newton Method for Semi-Infinite Minimax Problems
Journal of Applied Mathematics, 2014 Based on discretization methods for solving semi-infinite programming problems, this paper presents a spline smoothing Newton method for semi-infinite minimax problems. The spline smoothing technique uses a smooth cubic spline instead of max function and
Li Dong, Bo Yu, Yu Xiao
doaj +1 more source
Applied Sciences, 2020 Smoothing is one of the fundamental procedures in functional data analysis (FDA). The smoothing parameter λ influences data smoothness and fitting, which is governed by selecting automatic methods, namely, cross-validation (CV) and generalized ...
Muhammad Athif Mat Zin +3 more
doaj +1 more source
Applied Sciences, 2021 Meta-model sre generally applied to approximate multi-objective optimization, reliability analysis, reliability based design optimization, etc., not only in order to improve the efficiencies of numerical calculation and convergence, but also to ...
Chang-Yong Song
doaj +1 more source
Calibration of Computational Models With Categorical Parameters and Correlated Outputs via Bayesian Smoothing Spline ANOVA [PDF]
, 2014 It has become commonplace to use complex computer models to predict outcomes in regions where data do not exist. Typically these models need to be calibrated and validated using some experimental data, which often consists of multiple correlated outcomes.
C. Storlie +4 more
semanticscholar +1 more source
A comparison of spatial analysis methods for the construction of topographic maps of retinal cell density. [PDF]
PLoS ONE, 2014 Topographic maps that illustrate variations in the density of different neuronal sub-types across the retina are valuable tools for understanding the adaptive significance of retinal specialisations in different species of vertebrates. To date, such maps
Eduardo Garza-Gisholt +3 more
doaj +1 more source
Smoothing Spline ANOVA Models and their Applications in Complex and Massive Datasets
Topics in Splines and Applications, 2018 Complex and massive datasets can be easily accessed using the newly developed data acquisition technology. In spite of the fact that the smoothing spline ANOVA models have proven to be useful in a variety of fields, these datasets impose the challenges ...
Jingyi Zhang +5 more
semanticscholar +1 more source
Use of nonparametric regression methods for developing a local stem form model
Journal of Forest Science, 2014 A local mean stem curve of spruce was represented using regression splines. Abilities of smoothing spline and P-spline to model the mean stem curve were evaluated using data of 85 carefully measured stems of Norway spruce. For both techniques the optimal
K. Kuželka, R. Marušák
doaj +1 more source
A-Spline Regression for Fitting a Nonparametric Regression Function with Censored Data
Stats, 2020 This paper aims to solve the problem of fitting a nonparametric regression function with right-censored data. In general, issues of censorship in the response variable are solved by synthetic data transformation based on the Kaplan–Meier estimator in the
Ersin Yılmaz +2 more
doaj +1 more source
Estimating conditional heteroscedastic nonlinear autoregressive model by using smoothing spline and penalized spline methods [PDF]
Songklanakarin Journal of Science and Technology (SJST), 2019 We propose smoothing spline (SS) and penalized spline (PS) methods in a class of nonparametric regression methods for estimating the unknown functions in a conditional heteroscedastic nonlinear autoregressive (CHNLAR) model.
Autcha Araveeporn
doaj +1 more source
Efficient algorithms for robust generalized cross-validation spline smoothing
, 2010 Generalized cross-validation (GCV) is a widely used parameter selection criterion for spline smoothing, but it can give poor results if the sample size n is not sufficiently large.
Anderssen, R.S. +5 more
core +1 more source