Results 21 to 30 of about 204,377 (277)
Loss and risk in smoothing parameter selection [PDF]
Journal of Nonparametric Statistics, 1994 For several years there has been debate over the relative merits of loss and risk as measures of the performance of nonparametric density estimators. In the way that this debate has dealt with risk, it has largely ignored the fact that any practical bandwidth selection rule must produce a random bandwidth. Existing theory for risk of density estimators
Birgit Grund, Peter Hall, J. S. Marron
openaire +1 more source
Functional principal components analysis via penalized rank one approximation [PDF]
, 2008 Two existing approaches to functional principal components analysis (FPCA) are due to Rice and Silverman (1991) and Silverman (1996), both based on maximizing variance but introducing penalization in different ways.
Buja, Andreas +2 more
core +5 more sources
Design of output fluctuation smoothing strategy in photovoltaic power station [PDF]
E3S Web of Conferences, 2020 The output power of photovoltaic (PV) power station has strong fluctuation and randomness. Large-scale photovoltaic grid connection will affect the safe operation of power grid.
Zhang Yu +5 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
Univariate and bivariate distribution of growth traits in beef buffaloes from Brazil
Italian Journal of Animal Science, 2010 The aim of this study was to analyze the weight at birth (BW) and adjusted at 205 (W205), 365 (W365) and 550 (W55O) days in beef buffaloes from Brazil, using two approaches: parametric, by normal distribution, and non-parametric, by kernel function, and ...
J.C. de Souza +5 more
doaj +1 more source
Geodesy and Geodynamics, 2021 When the total least squares (TLS) solution is used to solve the parameters in the errors-in-variables (EIV) model, the obtained parameter estimations will be unreliable in the observations containing systematic errors.
Leyang Wang, Luyun Xiong, Tao Chen
doaj +1 more source
Simultaneous selection of variables and smoothing parameters by genetic algorithms [PDF]
, 2004 In additive models the problem of variable selection is strongly linked to the choice of the amount of smoothing used for components that represent metrical variables.
Krause, Rüdiger, Tutz, Gerhard
core +2 more sources
Optimal parameter selection for intensity-based multi-sensor data registration [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2014 Accurate co-registration of multi-sensor data is a primary step in data integration for photogrammetric and remote sensing applications. A proven intensity-based registration approach is Mutual Information (MI).
E. G. Parmehr +3 more
doaj +1 more source
lpdensity: Local Polynomial Density Estimation and Inference
Journal of Statistical Software, 2022 Density estimation and inference methods are widely used in empirical work. When the underlying distribution has compact support, conventional kernel-based density estimators are no longer consistent near or at the boundary because of their well-known ...
Matias D. Cattaneo +2 more
doaj +1 more source
Approximate Interpolation with Applications to Selecting Smoothing Parameters
Numerische Mathematik, 2005 An approximation problem can be shortly stated as follows: for a finite set \(X\) of points situated in a bounded set \(\Omega\) and a corresponding data values of an unknown function \(f \in C(\Omega)\), a function \(s_{f,X} \in C(\Omega)\) to produce a good approximation is required.
Wendland, Holger, Rieger, C.
openaire +3 more sources