Add to ORKGEstimation of distributions of stochastic models is studied and adaptive selection of a better of two estimates is considered. Devroye and Lugosi's recent book on this topic established a theorem on the total variation error of a selection rule proposed by them. We extended this theorem to arbitrary metric divergence errors, and also to more general alternative selection rules
In this paper, we propose a new criterion for selection between nested models. We suppose that the c...
Recently, Hjort and Claeskens (2003) developed an asymptotic theory for model selection, model avera...
In model selection problems, robustness is one important feature for selecting an adequate model fro...
Abstract: The aim of this work is to develop a new model selection criterion using a general discrep...
The role of the selection operation-that stochastically discriminate between individuals based on th...
In this note we introduce some divergence-based model selection criteria. These criteria are defined...
Although robust divergence, such as density power divergence and γ-divergence, is helpful for robust...
This paper addresses the problem of deriving the asymptotic distribution of the empirical distributi...
We study model selection strategies based on penalized empirical loss minimization. We point out a...
It is often necessary to make sampling-based statistical inference about many probability distributi...
How can we select the best performing data-driven model? How can we rigorously estimate its generali...
There are many statistics which can be used to characterize data sets and provide valuable informati...
Plug-in estimation and corresponding refinements involving penalisation have been considered in vari...
To perform inference after model selection, we propose controlling the selective type I error; i.e.,...
Model selection is of fundamental importance to high dimensional modelling featured in many contempo...
In this paper, we propose a new criterion for selection between nested models. We suppose that the c...
Recently, Hjort and Claeskens (2003) developed an asymptotic theory for model selection, model avera...
In model selection problems, robustness is one important feature for selecting an adequate model fro...
Abstract: The aim of this work is to develop a new model selection criterion using a general discrep...
The role of the selection operation-that stochastically discriminate between individuals based on th...
In this note we introduce some divergence-based model selection criteria. These criteria are defined...
Although robust divergence, such as density power divergence and γ-divergence, is helpful for robust...
This paper addresses the problem of deriving the asymptotic distribution of the empirical distributi...
We study model selection strategies based on penalized empirical loss minimization. We point out a...
It is often necessary to make sampling-based statistical inference about many probability distributi...
How can we select the best performing data-driven model? How can we rigorously estimate its generali...
There are many statistics which can be used to characterize data sets and provide valuable informati...
Plug-in estimation and corresponding refinements involving penalisation have been considered in vari...
To perform inference after model selection, we propose controlling the selective type I error; i.e.,...
Model selection is of fundamental importance to high dimensional modelling featured in many contempo...
In this paper, we propose a new criterion for selection between nested models. We suppose that the c...
Recently, Hjort and Claeskens (2003) developed an asymptotic theory for model selection, model avera...
In model selection problems, robustness is one important feature for selecting an adequate model fro...