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Model Selection and Model Averaging
Given a data set, you can fit thousands of models at the push of a button, but how do you choose the best? With so many candidate models, overfitting is a real danger. Is the monkey who typed HamletExpand
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Robust and efficient estimation by minimising a density power divergence
A minimum divergence estimation method is developed for robust parameter estimation. The proposed approach uses new density-based divergences which, unlike existing methods of this type such asExpand
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Frequentist Model Average Estimators
The traditional use of model selection methods in practice is to proceed as if the final selected model had been chosen in advance, without acknowledging the additional uncertainty introduced byExpand
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Nonparametric Bayes Estimators Based on Beta Processes in Models for Life History Data
The article studies the problem of finding Bayes estimators for cumulative hazard rates and related quantities, w.r.t. prior distributions that correspond to cumulative hazard rate processes withExpand
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The Focused Information Criterion
A variety of model selection criteria have been developed, of general and specific types. Most of these aim at selecting a single model with good overall properties, for example, formulated viaExpand
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Locally parametric nonparametric density estimation
This paper develops a nonparametric density estimator with parametric overtones. Suppose f(x, θ) is some family of densities, indexed by a vector of parameters θ. We define a local kernel-smoothedExpand
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Model Selection and Model Averaging: Contents
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Asymptotics for minimisers of convex processes
By means of two simple convexity arguments we are able to develop a gen- eral method for proving consistency and asymptotic normality of estimators that are deflned by minimisation of convexExpand
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Nonparametric Density Estimation with a Parametric Start
The traditional kernel density estimator of an unknown density is by construction completely nonparametric in the sense that it has no preferences and will work reasonably well for all shapes. TheExpand
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Extending the Scope of Empirical Likelihood
This article extends the scope of empirical likelihood methodology ill three directions: to allow for plug-in estimates Of nuisance parameters in estimating equations, slower than root n-rates ofExpand
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