One-sided tests in shared frailty models (2008)
CLAESKENS, Gerda, NGUTI, Rosemary, JANSSEN, Paul
Tests for the presence of heterogeneity in frailty models use an alternative hypothesis in which the heterogeneity parameter is subject to an inequality constraint. As a result, the classical...
Bayesian-motivated tests of function fit and their asymptotic frequentist properties (2005)
Aerts, Marc, Claeskens, Gerda, Hart, Jeffrey D.
We propose and analyze nonparametric tests of the null hypothesis that a function belongs to a specified parametric family. The tests are based on BIC approximations, \pi_{BIC}, to the posterior...
Bayesian-motivated tests of function fit and their asymptotic frequentist properties (2004)
Aerts, Marc, Claeskens, Gerda, Hart, Jeffrey D.
We propose and analyze nonparametric tests of the null hypothesis that a function belongs to a specified parametric family. The tests are based on BIC approximations, πBIC, to the posterior...
Bootstrap confidence bands for regression curves and their derivatives (2003)
Claeskens, Gerda, Keilegom, Ingrid Van
Confidence bands for regression curves and their first p derivatives are obtained via local pth order polynomial estimation. The method allows for multiparameter local likelihood estimation as well...
A quadratic bootstrap method and improved estimation in logistic regression (2003)
This paper presents a quadratic one-step bootstrap method for binary response data. Rather than resampling from the original sample, the proposed method resamples summands appearing in the quadratic...
A quadratic bootstrap method and improved estimation in logistic regression (2003)
This paper presents a quadratic one-step bootstrap method for binary response data. Rather than resampling from the original sample, the proposed method resamples summands appearing in the quadratic...
Two lack of fit tests for multiple logistic regression (2003)
AERTS, Marc, AERTS, Marc, CLAESKENS, Gerda, HART, Jeff, MOONS, Elke, WETS, Geert
A quadratic bootstrap method and improved estimation in logistic regression (2003)
CLAESKENS, Gerda, AERTS, Marc, MOLENBERGHS, Geert
This paper presents a quadratic one-step bootstrap method for binary response data. Rather than resampling from the original sample, the proposed method resamples summands appearing in the quadratic...
Two lack of fit tests for multiple logistic regression (2003)
AERTS, Marc, AERTS, Marc, CLAESKENS, Gerda, HART, Jeff, MOONS, Elke, WETS, Geert
Effect of dependence on stochastic measures of accuracy of density estimations (2002)
In kernel density estimation, those data values that make a nondegenerate contribution to the estimator (computed at a given point) tend to be spaced well apart. This property has the effect of...
Some theory for penalized spline additive models (2002)
Claeskens, Gerda, Wand, Matthew P.
Generalized additive models have become one of the most widely used modern statistical tools. Traditionally, they are fit through scatterplot smoothing and the backfitting algorithm. However, a more...
Local multiple imputation (2002)
Dealing with missing data via parametric multiple imputation methods usually implies stating several strong assumptions both about the distribution of the data and about underlying regression...
A note on the quadratic bootstrap and improved estimation in logistic regression (2002)
This paper presents a quadratic one-step bootstrap method for binary response data. Rather than resampling from the original sample, the proposed method resamples summands appearing in the quadratic...
Some theory for penalized spline additive models (2002)
Claeskens, Gerda, Wand, Matthew P.
Generalized additive models have become one of the most widely used modern statistical tools. Traditionally, they are fit through scatterplot smoothing and the backfitting algorithm. However, a more...
Local multiple imputation (2002)
Dealing with missing data via parametric multiple imputation methods usually implies stating several strong assumptions both about the distribution of the data and about underlying regression...
A note on the quadratic bootstrap and improved estimation in logistic regression (2002)
This paper presents a quadratic one-step bootstrap method for binary response data. Rather than resampling from the original sample, the proposed method resamples summands appearing in the quadratic...
Some theory for penalized spline additive models (2002)
AERTS, Marc, CLAESKENS, Gerda, Wand, Matthew P.
Generalized additive models have become one of the most widely used modern statistical tools. Traditionally, they are fit through scatterplot smoothing and the backfitting algorithm. However, a more...
Robust benchmark dose determination based on profile score methods (2002)
AERTS, Marc, CLAESKENS, Gerda, MOLENBERGHS, Geert, Ryan, Louise
Local multiple imputation (2002)
AERTS, Marc, CLAESKENS, Gerda, HENS, Niel, MOLENBERGHS, Geert
Dealing with missing data via parametric multiple imputation methods usually implies stating several strong assumptions both about the distribution of the data and about underlying regression...
A note on the quadratic bootstrap and improved estimation in logistic regression (2002)
CLAESKENS, Gerda, AERTS, Marc, MOLENBERGHS, Geert
This paper presents a quadratic one-step bootstrap method for binary response data. Rather than resampling from the original sample, the proposed method resamples summands appearing in the quadratic...
Bootstrap tests for misspecified models, with application to clustered binary data (2001)
When the data do not come from the assumed parametric model, the usual asymptotic chi-squared distribution under the null hypothesis, remains valid for "robustified" Wald and score test statistics....
Bootstrap tests for misspecified models, with application to clustered binary data (2001)
When the data do not come from the assumed parametric model, the usual asymptotic chi-squared distribution under the null hypothesis, remains valid for "robustified" Wald and score test statistics....
Bootstrap tests for misspecified models, with application to clustered binary data (2001)
When the data do not come from the assumed parametric model, the usual asymptotic chi-squared distribution under the null hypothesis, remains valid for "robustified" Wald and score test statistics....
Multiple nonparametric bootstrap imputation (2001)
HENS, Niel, AERTS, Marc, CLAESKENS, Gerda, MOLENBERGHS, Geert
Bootstrapping local polynomial estimators in likelihood-based models (2000)
The local likelihood estimator and a semiparametric bootstrap method are studied under weaker conditions than usual; it is not assumed that the true probability distribution underlying the...
On local estimating equations in additive multiparameter models (2000)
http://dx.doi.org/10.1016/S0167-7152(00)00042-0
Bootstrapping local polynomial estimators in likelihood-based models (2000)
The local likelihood estimator and a semiparametric bootstrap method are studied under weaker conditions than usual; it is not assumed that the true probability distribution underlying the...
On local estimating equations in additive multiparameter models (2000)
http://dx.doi.org/10.1016/S0167-7152(00)00042-0
Bootstrapping local polynomial estimators in likelihood-based models (2000)
The local likelihood estimator and a semiparametric bootstrap method are studied under weaker conditions than usual; it is not assumed that the true probability distribution underlying the...
On local estimating equations in additive multiparameter models (2000)
http://dx.doi.org/10.1016/S0167-7152(00)00042-0
Some Results on Penalized Spline Estimation in Generalized Additive and Semiparametric Models (1999)
Gerda Claeskens, Marc Aerts, M. P. Wand
Introduction In spline regression models, the smoothness of the #tted model depends on the knots via their location and how many knots there are. An alternative to knot selection is to keep all the...
Testing the fit of a parametric function (1999)
Claeskens, Gerda, Hart, Jeffrey
General methods for testing the fit of a parametric function are proposed. The idea underlying each method is to ``accept'' the prescribed parametric model if and only if it is chosen by a model...
Some results on penalized spline estimation in generalized additive and semiparametric models (1999)
Testing the fit of a parametric function (1999)
Claeskens, Gerda, Hart, Jeffrey
General methods for testing the fit of a parametric function are proposed. The idea underlying each method is to ``accept'' the prescribed parametric model if and only if it is chosen by a model...
Some results on penalized spline estimation in generalized additive and semiparametric models (1999)
Testing the fit of a parametric function (1999)
AERTS, Marc, CLAESKENS, Gerda, HART, Jeffrey
General methods for testing the fit of a parametric function are proposed. The idea underlying each method is to ``accept'' the prescribed parametric model if and only if it is chosen by a model...
Some results on penalized spline estimation in generalized additive and semiparametric models (1999)
Analysis of clustered multivariate data from developmental toxicity studies (1998)
MOLENBERGHS, Geert, GEYS, Helena, DECLERCK, Lieven, CLAESKENS, Gerda, AERTS, Marc
Local polynomial estimation in multiparameter likelihood models (1997)
The purpose of this article is to extend local oneparameter models to multiparameter likelihood models. The main motivation is the need for nonparametric alternatives to parametric dose-response...
Local polynomial estimation in multiparameter likelihood models (1997)
The purpose of this article is to extend local oneparameter models to multiparameter likelihood models. The main motivation is the need for nonparametric alternatives to parametric dose-response...
Local polynomial estimation in multiparameter likelihood models (1997)
The purpose of this article is to extend local oneparameter models to multiparameter likelihood models. The main motivation is the need for nonparametric alternatives to parametric dose-response...
Restricted likelihood ratio lack-of-fit tests using mixed spline models
Penalized regression spline models afford a simple mixed model representation in which variance components control the degree of non-linearity in the smooth function estimates. This motivates the...
MINIMIZING AVERAGE RISK IN REGRESSION MODELS
Claeskens, Gerda, Hjort, Nils Lid
Most model selection mechanisms work in an overall modus, providing models without specific concern for how the selected model is going to be used afterward. The focused information criterion (FIC),...
Goodness of Fit via Non-parametric Likelihood Ratios
Gerda Claeskens, Nils Lid Hjort
To test if a density "f" is equal to a specified "f"0, one knows by the Neyman-Pearson lemma the form of the optimal test at a specified alternative "f"1. Any non-parametric density estimation scheme...
One-sided tests in shared frailty models
Gerda Claeskens, Rosemary Nguti, Paul Janssen
Inference under inequality constraints, Frailty models, Likelihood ratio test, Mixture of χ 2-distributions, Score test, Survival data, 62E20, 62N03, 62F03,
Exact likelihood ratio tests for penalised splines
Ciprian Crainiceanu, David Ruppert, Gerda Claeskens, M. P. Wand
Penalised-spline-based additive models allow a simple mixed model representation where the variance components control departures from linear models. The smoothing parameter is the ratio of the...
Bootstrapping Pseudolikelihood Models for Clustered Binary Data
Clustered binary data, developmental toxicity, exponential family, parametric bootstrap, pseudolikelihood,