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Reconciling Model Selection and Prediction
AIC BIC Consistency Contiguity Local alternative Minimax-rate optimality
2010/3/18
It is known that there is a dichotomy in the performance of model selectors.
Those that are consistent (having the “oracle property”) do not achieve the
asymptotic minimax rate for prediction error....
Bayesian Model Averaging and Bayesian Predictive Information Criterion for Model Selection
Bayesian model averaging Bayesian predictive information criterion Markov chain Monte Carlo
2009/3/5
The problem of evaluating the goodness of the predictive distributions developed by the Bayesian model averaging approach is investigated. Considering the maximization of the posterior mean of the exp...
Generalized Information Criteria in Model Selection for Locally Stationary Processes
Generalized information criterion locally stationary process minimum distance estimation misspecified models time varying spectral density
2009/3/5
The problem of fitting a parametric model of time series with time varying parameters attracts our attention. We evaluate a goodness of time varying spectral models from an information theoretic point...
Efficient Estimation and Model Selection for Grouped Data with Local Moments
AIC GMM grouped data local moments MLE model select
2009/3/5
This paper proposes efficient estimation methods of unknown parameters when frequencies as well as local moments are available in grouped data. Assuming the original data is an i.i.d. sample from a pa...
Some limit properties for information based model selection criteria are given in the context of unit root evaluation and various assumptions about initial conditions. Allowing for a nonparametric sho...
Bayesian Computation and Model Selection in Population Genetics
Bayesian Computation Model Selection Population Genetics
2010/3/17
Until recently, the use of Bayesian inference in population genetics was lim-
ited to a few cases because for many realistic population genetic models the
likelihood function cannot be calculated an...
Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems
Approximate Bayesian computation scheme parameter inference model selection dynamical systems
2010/3/17
Approximate Bayesian computation methods can be used to evaluate posterior distributions without having to calculate likelihoods. In this paper we discuss and apply an approximate Bayesian computation...
Model selection for density estimation with L2-loss
Density estimation L2-loss model selection estimator selection histograms
2010/4/30
We consider here estimation of an unknown probability density s belonging
to L2(μ) where μ is a probability measure. We have at hand n i.i.d. observations
with density s and use the squared L2-norm ...
Formal and Informal Model Selection with Incomplete Data
Interval of ignorance linear mixed model missing at random missing not at random multivariate normal sensitivity analysis
2010/4/30
Model selection and assessment with incomplete data pose
challenges in addition to the ones encountered with complete data.
There are two main reasons for this. First, many models describe character...
Fence methods for mixed model selection
Adaptive fence consistency F-B fence finite sample performance GLMM linear mixed model model selection
2010/4/30
Many model search strategies involve trading off model fit with
model complexity in a penalized goodness of fit measure. Asymptotic
properties for these types of procedures in settings like linear
...
Can One Estimate The Unconditional Distribution of Post-Model-Selection Estimators?
Inference after model selection Post-model-selection estimator Pre-test estimator Selection of regressors
2010/4/28
We consider the problem of estimating the unconditional distribution of a post-model-selection estimator.The notion of a post-model-selection estimator here refers to the combined procedure resulting ...
The Loss Rank Principle for Model Selection
Model selection loss rank principle non-parametric regression classification general loss function k nearest neighbors
2010/4/27
We introduce a new principle for model selection in regression and classification.
Many regression models are controlled by some smoothness or flexibility
or complexity parameter c, e.g. the number ...
Can one estimate the conditional distribution of post-model-selection estimators?
Inference after model selection post-model-selection estimator pre-test estimator selection of regressors
2010/4/27
We consider the problem of estimating the conditional distribution
of a post-model-selection estimator where the conditioning is on
the selected model. The notion of a post-model-selection estimator...
An improved method for model selection based on Information Criteria
improved method model selection Information Criteria
2010/4/26
Information criteria are an appropriate and widely
used tool for solving model selection problems. However, different
ways to use them exist, each leading to a more or less precise
approximation of...
Model selection by resampling penalization。