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A KNOWLEDGE-BASED VARIABLE SELECTION METHOD FOR BOX-COX TRANSFORMATION
Regression analysis Box-Cox transformation variable selection j-th best subset problem variable classification meaningful subset practically best regression equation intellectual statistical system OEPP
2009/3/10
In actual applications of regression analysis, users face two difficult problems. One is to find the most appropriate functional form, while the other is to search for the best subset derivable from a...
Free-knot Splines and Adaptive Knot Selection
adaptive model selection evolutionary algorithms inhomogeneous smoothness non-parametric regression signal processing spatial adaptation variable multiple knots
2009/3/9
Conventional spline procedures have proven to be effective and useful for estimating smooth functions. However, these procedures find piecewise and inhomogeneous smooth functions difficult to handle. ...
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...
On the Selection of Irregular，Misspecified Regression Models：a Comment on Folklore
Consistency misspecified models model selection regression
2009/3/5
In this paper we will investigate the consequences of applying model selection methods under regularity conditions that are sufficiently general to encompass (i) stochastic models involving non-statio...
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...
Sparse partial least squares for on-line variable selection in multivariate data streams
Sparse partial least squares on-line variable selection multivariate data streams
2010/3/18
In this paper we propose a computationally efficient algorithm for on-line variable
selection in multivariate regression problems involving high dimensional data streams.
The algorithm recursively e...
Bayesian projection approaches to variable selection and exploring model uncertainty
Bayesian variable selection Kullback-Leibler projection lasso non-negative garotte preconditioning
2010/3/17
A Bayesian approach to variable selection which is based on the expected Kullback-
Leibler divergence between the full model and its projection onto a submodel has
recently been suggested in the lit...
Bayesian multinomial regression with class-specific predictor selection
Bayesian model averaging classification Markov chain MonteCarlo multinomial models
2010/3/17
Consider a multinomial regression model where the response,
which indicates a unit’s membership in one of several possible unordered
classes, is associated with a set of predictor variables. Such
m...
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...
The sparsity and bias of the Lasso selection in high-dimensional linear regression
Penalized regression high-dimensional data variable selection bias rate consistency spectral analysis random matrices
2010/4/30
Meinshausen and Buhlmann [Ann. Statist. 34 (2006) 1436–1462]
showed that, for neighborhood selection in Gaussian graphical models,
under a neighborhood stability condition, the LASSO is consistent,
...