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Predictive stability criteria for penalty selection in linear models
Choosing a shrinkage method can be done by selecting a penalty from a list of pre-specified penalties or by constructing a penalty based on the data....
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Efficient information-based criteria for model selection in quantile regression
Information-based model selection criteria such as the AIC and BIC employ check loss functions to measure the goodness of fit for quantile regression...
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Model Selection and Regularization
This chapter presents regularization and selection methods for linear and nonlinear (parametric)Parametric models. These are important Machine...
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Bootstrap for inference after model selection and model averaging for likelihood models
A one-step semiparametric bootstrap procedure is constructed to estimate the distribution of estimators after model selection and of model averaging...
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Post-Model-Selection Prediction Intervals for Generalized Linear Models
We give two prediction intervals for Generalized Linear Models that take model selection uncertainty into account. The first is a straightforward...
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Using Model Selection Criteria to Choose the Number of Principal Components
The use of information criteria, especially AIC (Akaike’s information criterion) and BIC (Bayesian information criterion), for choosing an adequate...
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On variable selection in a semiparametric AFT mixture cure model
In clinical studies, one often encounters time-to-event data that are subject to right censoring and for which a fraction of the patients under study...
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Bayesian generalized additive model selection including a fast variational option
We use Bayesian model selection paradigms, such as group least absolute shrinkage and selection operator priors, to facilitate generalized additive...
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Information criteria bias correction for group selection
The main contribution of this paper lies in the extension towards group lasso of a Mallows’ Cp-like information criterion used in finetuning the...
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Model-X Knockoffs for high-dimensional controlled variable selection under the proportional hazards model with heterogeneity parameter
A major challenge arising from data integration pertains to data heterogeneity in terms of study population, study design, or study coordination....
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Variable selection in proportional odds model with informatively interval-censored data
The proportional odds (PO) model is one of the most commonly used models for regression analysis of failure time data in survival analysis. It...
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Adaptive optimal stimulus selection in cognitive models using a model averaging approach
Stimulus selection based on the maximum Fisher information (MFI) principle enables the efficient estimation of participant parameters of cognitive...
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Bayesian Model Selection for Longitudinal Count Data
We explore the performance of three popular model-selection criteria for generalised linear mixed-effects models (GLMMs) for longitudinal count data...
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Variable selection for nonparametric quantile regression via measurement error model
This paper proposes a variable selection procedure for the nonparametric quantile regression based on the measurement error model (MEM). The “false”...
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Model Selection
Often it is not clear which model you should use for the data at hand—maybe because it is not known ahead of time which combination of variables...
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A Bayesian approach for clustering and exact finite-sample model selection in longitudinal data mixtures
We consider mixtures of longitudinal trajectories, where one trajectory contains measurements over time of the variable of interest for one...
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A sequential feature selection procedure for high-dimensional Cox proportional hazards model
Feature selection for the high-dimensional Cox proportional hazards model (Cox model) is very important in many microarray genetic studies. In this...
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Subdata selection algorithm for linear model discrimination
A statistical method is likely to be sub-optimal if the assumed model does not reflect the structure of the data at hand. For this reason, it is...
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Regularization and variable selection in Heckman selection model
Sample selection arises when the outcome of interest is partially observed in a study. A common challenge is the requirement for exclusion...