Pages that link to "Item:Q2736873"
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The following pages link to Variable selection for regression models (Q2736873):
Displaying 50 items.
- Quantifying uncertainty in transdimensional Markov chain Monte Carlo using discrete Markov models (Q142140) (← links)
- Bayesian variable selection and estimation for group Lasso (Q273646) (← links)
- Large Bayesian VARMAs (Q281043) (← links)
- Minimizing variable selection criteria by Markov chain Monte Carlo (Q333351) (← links)
- Selection of polynomial chaos bases via Bayesian model uncertainty methods with applications to sparse approximation of PDEs with stochastic inputs (Q348681) (← links)
- Filter-type variable selection based on information measures for regression tasks (Q405995) (← links)
- A new heterogeneous multidimensional unfolding procedure (Q418407) (← links)
- Model selection in binary and Tobit quantile regression using the Gibbs sampler (Q433242) (← links)
- Frailty modeling via the empirical Bayes-Hastings sampler (Q434878) (← links)
- Parallel hierarchical sampling: a general-purpose interacting Markov chains Monte Carlo algorithm (Q434903) (← links)
- Investigating international new product diffusion speed: A semiparametric approach (Q439153) (← links)
- Bayesian identification of protein differential expression in multi-group isobaric labelled mass spectrometry data (Q470303) (← links)
- Assessing the risk of rising temperature on brook trout: a spatial dynamic linear risk model (Q484605) (← links)
- Variable selection and functional form uncertainty in cross-country growth regressions (Q528109) (← links)
- Bayesian methods to overcome the winner's curse in genetic studies (Q542467) (← links)
- Reuse, recycle, reweigh: combating influenza through efficient sequential Bayesian computation for massive data (Q542932) (← links)
- A Bayesian lasso via reversible-jump MCMC (Q553732) (← links)
- Consistency of spike and slab regression (Q645450) (← links)
- A variant of AIC based on the Bayesian marginal likelihood (Q721607) (← links)
- On Bayesian lasso variable selection and the specification of the shrinkage parameter (Q746286) (← links)
- An efficient sampling algorithm with adaptations for Bayesian variable selection (Q889341) (← links)
- Computation for intrinsic variable selection in normal regression models via expected-posterior prior (Q892429) (← links)
- Bayesian regularization via graph Laplacian (Q899032) (← links)
- FSR methods for second-order regression models (Q901592) (← links)
- Comparison of Bayesian objective procedures for variable selection in linear regression (Q1019478) (← links)
- Variable selection in regression models using nonstandard optimisation of information criteria (Q1020778) (← links)
- Bayesian inference in non-homogeneous Markov mixtures of periodic autoregressions with state-dependent exogenous variables (Q1023565) (← links)
- Bayesian model comparison for the order restricted RC association model (Q1048645) (← links)
- Selecting important independent variables in linear regression models (Q1113231) (← links)
- Modelling species abundance in a river by negative binomial hidden Markov models (Q1621340) (← links)
- Big data Bayesian linear regression and variable selection by normal-inverse-gamma summation (Q1631590) (← links)
- Objective Bayesian transformation and variable selection using default Bayes factors (Q1703858) (← links)
- Generalized biodiversity assessment by Bayesian nested random effects models with spyke-and-slab priors (Q1726751) (← links)
- A novel variational Bayesian method for variable selection in logistic regression models (Q1727887) (← links)
- Bayesian variable selection in linear regression (Q1733296) (← links)
- Variable selection and transformation in linear regression models (Q1779679) (← links)
- Variable selection for structural models (Q1866230) (← links)
- Variable selection for multivariate logistic regression models (Q1869079) (← links)
- Bayesian variable selection for logistic mixed model with nonparametric random effects (Q1927042) (← links)
- Bayesian variable selection in non-homogeneous hidden Markov models through an evolutionary Monte Carlo method (Q2008134) (← links)
- Objective Bayesian edge screening and structure selection for Ising networks (Q2141635) (← links)
- Objective Bayesian group variable selection for linear model (Q2155018) (← links)
- Bayesian variable selection for mixed effects model with shrinkage prior (Q2184407) (← links)
- Bayesian variable selection using cost-adjusted BIC, with application to cost-effective measurement of quality of health care (Q2270666) (← links)
- A nonparametric spatial test to identify factors that shape a microbiome (Q2291523) (← links)
- Bayesian indicator variable selection to incorporate hierarchical overlapping group structure in multi-omics applications (Q2291545) (← links)
- Adaptive hierarchical priors for high-dimensional vector autoregressions (Q2323380) (← links)
- On some variable selection procedures based on data for regression models (Q2369497) (← links)
- Spike and slab variable selection: frequentist and Bayesian strategies (Q2388355) (← links)
- A graph Laplacian prior for Bayesian variable selection and grouping (Q2416741) (← links)
