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Title:

Theory for penalised spline regression

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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 white-noise 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 white-noise 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. Minimize

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Title:

On asymptotic normality and variance estimation for nondifferentiable survey estimators

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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 design-based 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 replication-based design-consistent 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. Minimize

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article

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Title:

Model-assisted estimation for complex surveys using penalised splines

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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 design-based methods. Data-driven 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 two-phase survey with multiple auxiliary variables. Copyright 2005, Oxford University Press. Minimize

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Title:

Penalized Splines

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Estimation of finite population totals in the presence of auxiliary information is con-sidered. A class of estimators based on penalized spline regression is proposed. These estimators are weighted linear combinations of sample observations, with weights cali-brated to known control totals. Further, they allow straightforward extensions to mul-t...

Estimation of finite population totals in the presence of auxiliary information is con-sidered. A class of estimators based on penalized spline regression is proposed. These estimators are weighted linear combinations of sample observations, with weights cali-brated to known control totals. Further, they allow straightforward extensions to mul-tiple 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 design-based methods. Data-driven penalty selection Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2008-08-15

Source:

http://www.econ.kuleuven.be/public/ndbaf45/papers/pspline_model-assisted.pdf

http://www.econ.kuleuven.be/public/ndbaf45/papers/pspline_model-assisted.pdf Minimize

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text

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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. Minimize

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Title:

Data-driven Selection of the Spline Dimension in Penalized Spline Regression

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A number of criteria exist to select the penalty in penalized spline regression, but the selection of the number of spline basis functions has received much less attention in the literature. We propose to use a maximum likelihood-based criterion to select the number of basis functions in penalized spline regression. The criterion is easy to appl...

A number of criteria exist to select the penalty in penalized spline regression, but the selection of the number of spline basis functions has received much less attention in the literature. We propose to use a maximum likelihood-based criterion to select the number of basis functions in penalized spline regression. The criterion is easy to apply and we describe its theoretical and practical properties. The criterion is also extended to the generalized regression case. Key words: nonparametric regression, mixed model, maximum likelihood, knot selection. 1 Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2011-12-13

Source:

http://www.stat.colostate.edu/research/Technical%20Reports/2009/2009_3.pdf

http://www.stat.colostate.edu/research/Technical%20Reports/2009/2009_3.pdf Minimize

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text

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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. Minimize

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Title:

Generalized Cross-validation for Bandwidth Selection of Backfitting Estimates in Generalized Additive Models

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This article presents a modified Newton method for minimizing multidimensional bandwidth selection for estimation in generalized additive models. The method is based on the Generalized Cross-Validation criterion applied to backfitting estimates. The approach in particular is applicable to higher dimensional problems and provides a computationall...

This article presents a modified Newton method for minimizing multidimensional bandwidth selection for estimation in generalized additive models. The method is based on the Generalized Cross-Validation criterion applied to backfitting estimates. The approach in particular is applicable to higher dimensional problems and provides a computationally e#cient alternative to full grid search in such cases. The implementation of the proposed method requires the estimation of a number of auxiliary quantities, and simple estimators are suggested. Extensions to semiparamatric models and other bandwidth selections are discussed. Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-04-17

Source:

http://www.public.iastate.edu/~jopsomer/papers/GCV_calculation_paper.pdf

http://www.public.iastate.edu/~jopsomer/papers/GCV_calculation_paper.pdf Minimize

Document Type:

text

Language:

en

Subjects:

local polynomial regression ; Newton method. Acknowledgements Both authors acknowledge support of

local polynomial regression ; Newton method. Acknowledgements Both authors acknowledge support of Minimize

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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. Minimize

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Title:

Local likelihood estimation in Generalized Additive Models

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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 one-dimensional 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. Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-04-14

Source:

http://www.public.iastate.edu/~jopsomer/papers/Loclik_GAM.ps

http://www.public.iastate.edu/~jopsomer/papers/Loclik_GAM.ps Minimize

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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 Minimize

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Title:

Weighted Local Polynomial Regression, Weighted Additive Models and Local Scoring

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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). Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-04-14

Source:

http://www.public.iastate.edu/~jopsomer/papers/Local_scoring.ps

http://www.public.iastate.edu/~jopsomer/papers/Local_scoring.ps Minimize

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text

Language:

en

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Title:

Smoothing Parameter Selection Methods for Nonparametric Regression With . . .

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2013-10-14

Source:

http://www.public.iastate.edu/~jopsomer/papers/Spatial_GCV_Biom.pdf

http://www.public.iastate.edu/~jopsomer/papers/Spatial_GCV_Biom.pdf Minimize

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en

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Running title: Weighted Generalized Additive Models.

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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 unaffected 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. Additive models and generalized additive models are popular multivariate nonparametric regression techniques, widely used by statisticians and other scientists. While a number of different fitting methods are available for these models, the most popular one is backfitting, an iterative algorithm proposed by Friedman and Stuetzle (1981). Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2013-08-12

Source:

http://www.stat.colostate.edu/~jopsomer/papers/Local_scoring.pdf

http://www.stat.colostate.edu/~jopsomer/papers/Local_scoring.pdf Minimize

Document Type:

text

Language:

en

DDC:

310 Collections of general statistics *(computed)*

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