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1.
Theory for penalised spline regression
Title:
Theory for penalised spline regression
Author:
Peter Hall
;
J. D. Opsomer
Peter Hall
;
J. D. Opsomer
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Description:
Penalised spline regression is a popular new approach to smoothing, but its theoretical properties are not yet well understood. In this paper, mean squared error expressions and consistency results are derived by using a whitenoise model representation for the estimator. The effect of the penalty on the bias and variance of the estimator is dis...
Penalised spline regression is a popular new approach to smoothing, but its theoretical properties are not yet well understood. In this paper, mean squared error expressions and consistency results are derived by using a whitenoise model representation for the estimator. The effect of the penalty on the bias and variance of the estimator is discussed, both for general splines and for the case of polynomial splines. The penalised spline regression estimator is shown to achieve the optimal nonparametric convergence rateestablished by Stone (1982). Copyright 2005, Oxford University Press.
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Document Type:
article
URL:
http://hdl.handle.net/10.1093/biomet/92.1.105
http://hdl.handle.net/10.1093/biomet/92.1.105
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RePEc: Research Papers in Economics
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2.
On asymptotic normality and variance estimation for nondifferentiable survey estimators
Title:
On asymptotic normality and variance estimation for nondifferentiable survey estimators
Author:
Jianqiang C. Wang
;
J. D. Opsomer
Jianqiang C. Wang
;
J. D. Opsomer
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Description:
Survey estimators of population quantities such as distribution functions and quantiles contain nondifferentiable functions of estimated quantities. The theoretical properties of such estimators are substantially more complicated to derive than those of differentiable estimators. In this article, we provide a unified framework for obtaining the ...
Survey estimators of population quantities such as distribution functions and quantiles contain nondifferentiable functions of estimated quantities. The theoretical properties of such estimators are substantially more complicated to derive than those of differentiable estimators. In this article, we provide a unified framework for obtaining the asymptotic designbased properties of two common types of nondifferentiable estimators. Estimators of the first type have an explicit expression, while those of the second are defined only as the solution to estimating equations. We propose both analytical and replicationbased designconsistent variance estimators for both cases, based on kernel regression. The practical behaviour of the variance estimators is demonstrated in a simulation experiment. Copyright 2011, Oxford University Press.
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Document Type:
article
URL:
http://hdl.handle.net/10.1093/biomet/asq077
http://hdl.handle.net/10.1093/biomet/asq077
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RePEc: Research Papers in Economics
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3.
Modelassisted estimation for complex surveys using penalised splines
Title:
Modelassisted estimation for complex surveys using penalised splines
Author:
F. J. Breidt
;
G. Claeskens
;
J. D. Opsomer
F. J. Breidt
;
G. Claeskens
;
J. D. Opsomer
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Estimation of finite population totals in the presence of auxiliary information is considered. A class of estimators based on penalised spline regression is proposed. These estimators are weighted linear combinations of sample observations, with weights calibrated to known control totals. They allow straightforward extensions to multiple auxilia...
Estimation of finite population totals in the presence of auxiliary information is considered. A class of estimators based on penalised spline regression is proposed. These estimators are weighted linear combinations of sample observations, with weights calibrated to known control totals. They allow straightforward extensions to multiple auxiliary variables and to complex designs. Under standard design conditions, the estimators are design consistent and asymptotically normal, and they admit consistent variance estimation using familiar designbased methods. Datadriven penalty selection is considered in the context of unequal probability sampling designs. Simulation experiments show that the estimators are more efficient than parametric regression estimators when the parametric model is incorrectly specified, while being approximately as efficient when the parametric specification is correct. An example using Forest Health Monitoring survey data from the U.S. Forest Service demonstrates the applicability of the methodology in the context of a twophase survey with multiple auxiliary variables. Copyright 2005, Oxford University Press.
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Document Type:
article
URL:
http://hdl.handle.net/10.1093/biomet/92.4.831
http://hdl.handle.net/10.1093/biomet/92.4.831
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RePEc: Research Papers in Economics
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4.
Penalized Splines
Open Access
Title:
Penalized Splines
Author:
F. J. Breidt
;
J. D. Opsomer
;
G. Claeskens
F. J. Breidt
;
J. D. Opsomer
;
G. Claeskens
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Estimation of finite population totals in the presence of auxiliary information is considered. A class of estimators based on penalized spline regression is proposed. These estimators are weighted linear combinations of sample observations, with weights calibrated to known control totals. Further, they allow straightforward extensions to mult...
Estimation of finite population totals in the presence of auxiliary information is considered. A class of estimators based on penalized spline regression is proposed. These estimators are weighted linear combinations of sample observations, with weights calibrated to known control totals. Further, they allow straightforward extensions to multiple auxiliary variables and to complex designs. Under standard design conditions, the estimators are design consistent and asymptotically normal, and they admit consistent variance estimation using familiar designbased methods. Datadriven penalty selection
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The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20080815
Source:
http://www.econ.kuleuven.be/public/ndbaf45/papers/pspline_modelassisted.pdf
http://www.econ.kuleuven.be/public/ndbaf45/papers/pspline_modelassisted.pdf
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Document Type:
text
Language:
en
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Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
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URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.117.6584
http://www.econ.kuleuven.be/public/ndbaf45/papers/pspline_modelassisted.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.117.6584
http://www.econ.kuleuven.be/public/ndbaf45/papers/pspline_modelassisted.pdf
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5.
Weighted Local Polynomial Regression, Weighted Additive Models and Local Scoring
Open Access
Title:
Weighted Local Polynomial Regression, Weighted Additive Models and Local Scoring
Author:
J. D. Opsomer
;
G. Kauermann
J. D. Opsomer
;
G. Kauermann
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This article describes the asymptotic properties of local polynomial regression estimators for univariate and additive models when observation weights are included. The implications of these findings are discussed for local scoring estimators, a widely used class of estimators for generalized additive models described in Hastie and Tibshirani (1...
This article describes the asymptotic properties of local polynomial regression estimators for univariate and additive models when observation weights are included. The implications of these findings are discussed for local scoring estimators, a widely used class of estimators for generalized additive models described in Hastie and Tibshirani (1990).
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The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20090414
Source:
http://www.public.iastate.edu/~jopsomer/papers/Local_scoring.ps
http://www.public.iastate.edu/~jopsomer/papers/Local_scoring.ps
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Document Type:
text
Language:
en
Rights:
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
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URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.38.5878
http://www.public.iastate.edu/~jopsomer/papers/Local_scoring.ps
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.38.5878
http://www.public.iastate.edu/~jopsomer/papers/Local_scoring.ps
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6.
A Note on Local Scoring and Weighted Local Polynomial Regression in Generalized Additive Models
Open Access
Title:
A Note on Local Scoring and Weighted Local Polynomial Regression in Generalized Additive Models
Author:
J. D. Opsomer
;
Göran Kauermann
J. D. Opsomer
;
Göran Kauermann
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This article describes the asymptotic properties of local polynomial regression estimators for univariate and additive models when observation weights are included. Such weighted additive models are a crucial component of local scoring, the widely used estimation algorithm for generalized additive models described in Hastie and Tibshirani (1990)...
This article describes the asymptotic properties of local polynomial regression estimators for univariate and additive models when observation weights are included. Such weighted additive models are a crucial component of local scoring, the widely used estimation algorithm for generalized additive models described in Hastie and Tibshirani (1990). The statistical properties of the univariate local polynomial estimator are shown to be asymptotically una#ected by the weights. In contrast, the weights inflate the asymptotic variance of the additive model estimators. The implications of these findings for the local scoring estimators are discussed.
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The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20090417
Source:
http://www.public.iastate.edu/~jopsomer/papers/Local_scoring.pdf
http://www.public.iastate.edu/~jopsomer/papers/Local_scoring.pdf
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Document Type:
text
Language:
en
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Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
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URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.15.1540
http://www.public.iastate.edu/~jopsomer/papers/Local_scoring.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.15.1540
http://www.public.iastate.edu/~jopsomer/papers/Local_scoring.pdf
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7.
Local likelihood estimation in Generalized Additive Models
Open Access
Title:
Local likelihood estimation in Generalized Additive Models
Author:
Göran Kauermann
;
J. D. Opsomer
Göran Kauermann
;
J. D. Opsomer
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Description:
Generalized additive models are a popular class of multivariate nonparametric regression models, due in large part to the ease of use of the local scoring estimation algorithm. However, the theoretical properties of the local scoring estimator are poorly understood. In this article, we propose a local likelihood estimator for generalized additiv...
Generalized additive models are a popular class of multivariate nonparametric regression models, due in large part to the ease of use of the local scoring estimation algorithm. However, the theoretical properties of the local scoring estimator are poorly understood. In this article, we propose a local likelihood estimator for generalized additive models that is closely related to the local scoring estimator tted by local polynomial regression. We derive the statistical properties of the estimator and show that it achieves the same asymptotic convergence rate as a onedimensional local polynomial regression estimator. We also propose a wild bootstrap estimator for calculating pointwise condence intervals for the additive component functions. The practical behavior of the proposed estimator is illustrated through simulation experiments and an example. Keywords: backtting, bootstrapping, generalized additive models, local likelihood, local polynomial regression, local scoring, wild bo.
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The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20090414
Source:
http://www.public.iastate.edu/~jopsomer/papers/Loclik_GAM.ps
http://www.public.iastate.edu/~jopsomer/papers/Loclik_GAM.ps
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Document Type:
text
Language:
en
Subjects:
backtting ; bootstrapping ; generalized additive models ; local likelihood ; local polynomial regression ; local scoring ; wild bootstrap. 1
backtting ; bootstrapping ; generalized additive models ; local likelihood ; local polynomial regression ; local scoring ; wild bootstrap. 1
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DDC:
310 Collections of general statistics
(computed)
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URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.38.1425
http://www.public.iastate.edu/~jopsomer/papers/Loclik_GAM.ps
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.38.1425
http://www.public.iastate.edu/~jopsomer/papers/Loclik_GAM.ps
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8.
Model Averaging in Survey Estimation
Open Access
Title:
Model Averaging in Survey Estimation
Author:
Xiaoxi Li
;
J. D. Opsomer
;
Yj Ŷj
;
Xtj Yj
Xiaoxi Li
;
J. D. Opsomer
;
Yj Ŷj
;
Xtj Yj
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Description:
Model averaging is a widely used method as it accounts for uncertainties in model selection. However, its applications in survey estimation are much to be explored. We investigate a modelaveraging (MA) regression estimator for the population total, for the case of a nonparametric regression model. Different ways to obtain this estimator are ex...
Model averaging is a widely used method as it accounts for uncertainties in model selection. However, its applications in survey estimation are much to be explored. We investigate a modelaveraging (MA) regression estimator for the population total, for the case of a nonparametric regression model. Different ways to obtain this estimator are explored through simulation studies. KEY WORDS: Local polynomial regression, crossvalidation, regression estimation.
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The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20150113
Source:
http://www.amstat.org/sections/srms/proceedings/y2006/Files/JSM2006000651.pdf
http://www.amstat.org/sections/srms/proceedings/y2006/Files/JSM2006000651.pdf
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Document Type:
text
Language:
en
Subjects:
−1∑
−1∑
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URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.529.9542
http://www.amstat.org/sections/srms/proceedings/y2006/Files/JSM2006000651.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.529.9542
http://www.amstat.org/sections/srms/proceedings/y2006/Files/JSM2006000651.pdf
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9.
Bootstrapping for Penalized Spline Regression
Open Access
Title:
Bootstrapping for Penalized Spline Regression
Author:
Göran Kauermann
;
Gerda Claeskens
;
J. D. Opsomer
Göran Kauermann
;
Gerda Claeskens
;
J. D. Opsomer
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We describe and contrast several different bootstrapping procedures for penalized spline smoothers. The bootstrapping procedures considered are variations on existing methods, developed under two different probabilistic frameworks. Under the first framework, penalized spline regression is considered an estimation technique to find an unknown smo...
We describe and contrast several different bootstrapping procedures for penalized spline smoothers. The bootstrapping procedures considered are variations on existing methods, developed under two different probabilistic frameworks. Under the first framework, penalized spline regression is considered an estimation technique to find an unknown smooth function. The smooth function is represented in a high dimensional spline basis, with spline coefficients estimated in a penalized form. Under the second framework, the unknown function is treated as a realization of a set of random spline coefficients, which are then predicted in a linear mixed model. We describe how bootstrapping methods can be implemented under both frameworks, and we show in theory and through simulations and examples that bootstrapping provides valid inference in both cases. We compare the inference obtained under both frameworks, and conclude that the latter generally produces better results than the former. The bootstrapping ideas are extended to hypothesis testing, where parametric components in a model are tested against nonparametric alternatives.
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The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20090402
Source:
http://www.econ.kuleuven.be/public/ndbaf45/papers/kauermannclaeskensopsomer.pdf
http://www.econ.kuleuven.be/public/ndbaf45/papers/kauermannclaeskensopsomer.pdf
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Document Type:
text
Language:
en
Subjects:
‡ Key words ; Mixed Model ; Nonparametric Regression ; Resampling ; Nonparametric Hypothesis Test
‡ Key words ; Mixed Model ; Nonparametric Regression ; Resampling ; Nonparametric Hypothesis Test
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519 Probabilities & applied mathematics
(computed)
;
310 Collections of general statistics
(computed)
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http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.132.129
http://www.econ.kuleuven.be/public/ndbaf45/papers/kauermannclaeskensopsomer.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.132.129
http://www.econ.kuleuven.be/public/ndbaf45/papers/kauermannclaeskensopsomer.pdf
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10.
Smoothing Parameter Selection Methods for Nonparametric Regression With . . .
Open Access
Title:
Smoothing Parameter Selection Methods for Nonparametric Regression With . . .
Author:
M. Franciscofernández
;
J. D. Opsomer
M. Franciscofernández
;
J. D. Opsomer
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The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20131014
Source:
http://www.public.iastate.edu/~jopsomer/papers/Spatial_GCV_Biom.pdf
http://www.public.iastate.edu/~jopsomer/papers/Spatial_GCV_Biom.pdf
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http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.8.5186
http://www.public.iastate.edu/~jopsomer/papers/Spatial_GCV_Biom.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.8.5186
http://www.public.iastate.edu/~jopsomer/papers/Spatial_GCV_Biom.pdf
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(4) africa
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(1) feeding habits
(1) ge pmosfets
(1) generalized additive models
(1) infants
(1) interface
(1) iron
(1) kappa gate dielectrics
(1) key words
(1) layer
(1) local likelihood
(1) local scoring
(1) malnutrition
(1) memory
(1) multi phase survey estimation local scoring...
(1) natural resource survey
(1) newton method acknowledgements both authors...
(1) nonparametric
(1) nonparametric hypothesis test
(1) nonparametric hypothesis testing
(1) nonparametric regression
(1) oxides
(1) passivation
(1) physics and astronomy
(1) plutarch
(1) quality
(1) randomized controlled trials
(1) regression
(1) removal
(1) stunting
(1) temperature
(1) varia
(1) wafer surface
(1) wasting
(1) weaning
(1) wild bootstrap 1
(1) −1∑
Subject:
Dewey Decimal Classification (DDC)
(7) Statistics [31*]
(3) Mathematics [51*]
(2) Social problems & social services [36*]
(1) Economics [33*]
(1) Technology [60*]
(1) Engineering [62*]
(1) Agriculture [63*]
Dewey Decimal Classification (DDC):
Year of Publication
(15) 2009
(5) 2008
(4) 2005
(3) 1996
(2) 2011
(2) 2013
(1) 2000
(1) 2001
(1) 2004
(1) 2006
(1) 2015
Year of Publication:
Content Provider
(14) CiteSeerX
(5) Leuven KU: Lirias
(4) Tropical Medicine Inst. (ITM): TropMed Central...
(4) RePEc.org
(4) Ghent Univ.
(3) HighWire Press
(2) Cornell Univ.: eCommons
(2) Munich LMU: Open Access
(2) Bielefeld Univ.: Publications
Content Provider:
Language
(35) English
(5) Unknown
Language:
Document Type
(17) Text
(14) Article, Journals
(6) Reports, Papers, Lectures
(3) Books
Document Type:
Access
(23) Unknown
(17) Open Access
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