Results 11 to 20 of about 123 (106)

Integration and backfitting methods in additive models-finite sample properties and comparison [PDF]

open access: yes, 1998
Additive models, curse of dimensionality, dimensionality reduction, model choice, nonparametric regression, 62G07, 62G20, 62G35,
Stefan Sperlich   +10 more
core   +1 more source

Trimmed means for functional data [PDF]

open access: yes, 2001
Data depth, functional data, trimmed means estimates, 62G07, 62G05,
Fraiman, Ricardo   +3 more
core   +1 more source

Local polynomial regression smoothers with AR-error structure [PDF]

open access: yes, 2002
Nonparametric estimators, local polynomial fitting, autoregressive process, 62G07, 62H12, 62M09,
Vilar, Juan M.   +3 more
core   +1 more source

An empirical central limit theorem with applications to copulas under weak dependence

open access: yes, 2009
Copulas, Multivariate FCLT, Weak dependence, 62M10, 62G07, 60F17,
Jean-David Fermanian   +5 more
core   +1 more source

Asymptotic normality of the Parzen–Rosenblatt density estimator for strongly mixing random fields [PDF]

open access: yes, 2010
Central limit theorem, Kernel density estimator, Strongly mixing random fields, Spatial processes, 62G05, 62G07, 60G60,
El Machkouri, Mohamed   +1 more
core   +1 more source

Nonparametric density estimation in presence of bias and censoring

open access: yes, 2009
Adaptive estimation, Minimax rate, Biased data, Right-censoring, Nonparametric penalized contrast estimator, 62G07, 62N01,
Comte, Fabienne   +5 more
core   +1 more source

Choosing the smoothing parameter for unordered multinomial data

open access: yes, 1998
Bayes estimation, binomial data, cross-validation, mean squared error, shirnkage, unbiasedness, 62G07,
M. Jones   +3 more
core   +1 more source

Recursive local polynomial regression under dependence conditions [PDF]

open access: yes, 2000
Local polynomial fitting, recursive nonparametric estimation, strongly mixing processes, 62G07, 62H12, 62M09,
José Vilar-Fernández   +3 more
core   +1 more source

Asymptotic properties of wavelet estimators for derivative function in heteroscedastic regression model

open access: yesDemonstratio Mathematica
This paper considers a wavelet approach to derivative function estimation in heteroscedastic regression model. A linear wavelet estimator is constructed by using projection operator.
Guo Huijun, Kou Junke, Zhang Hao
doaj   +1 more source

Projection Estimates of Constrained Functional Parameters [PDF]

open access: yes, 2005
AMS classifications: 62G05; 62G07; 62G08; 62G20 ...
Segers, J.   +2 more
core   +1 more source

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