Variable selectionMinimum-distance methodsFinite mixture regression (FMR) models are frequently used in statistical modeling, often with many covariates with low significance. Variable selection techniques can be employed to identify the covariates with little influence on the response. The problem of ...
PosteriorMdl = estimate(PriorMdl,X,y) returns the model that characterizes the joint posterior distributions of β and σ2 of a Bayesian linear regression model. estimate also performs predictor variable selection. PriorMdl specifies the joint prior distribution of the parameters, the structure of th...
a polynomial regression model that excludes hierarchically inferior predictors (i.e., lower-order terms) is considered to be not well formulated. Existing variable-selection algorithms do not take into account the hierarchy of predictors and often ...
In this paper we extend existing Bayesian methods for variable selection in Gaussian process regression, to select both the regression terms and the active covariates in the spatial correlation structure. We then use the estimated posterior probabilities to choose between relatively few modes through ...
Febrero-Bande, M., Gonz´alez-Manteiga, W., & de la Fuente, M. O. (2017). Variable selection in functional additive regression models. In G. Aneiros, E. G. Bongiorno, R. Cao, & P. Vieu (Eds.), Functional Statistics and Related Fields (pp. 113-122). Cham: Springer Interna- ...
estimate Perform predictor variable selection for Bayesian linear regression models simulate Simulate regression coefficients and disturbance variance of Bayesian linear regression model forecast Forecast responses of Bayesian linear regression model plot Visualize prior and posterior densities of Bayesian linear re...
This example shows how to implement stochastic search variable selection (SSVS), a Bayesian variable selection technique for linear regression models. Introduction Consider this Bayesian linear regression model. yt=∑kβkxtk+εt. The regression coefficientsβk∣σ2∼N(μj,σ2Vk). ...
Create a prior model for SSVS. Specify the number of predictorsp. p = 3; VarNames = ["IPI""E""WR"]; PriorMdl = bayeslm(p,'ModelType','mixconjugateblm','VarNames',VarNames); PriorMdlis amixconjugateblmBayesian linear regression model object for SSVS predictor selection representing the...
Variable subset selection via GA and information complexity in mixtures of Poisson and negative binomial regression modelsCount data, for example the number of observed cases of a disease in a city, often arise in the fields of healthcare analytics and epidemiology. In this paper, we consider ...
Therefore, determining the variables that are important for the location and the scale is as important as estimating the parameters of these models. From this point of view, a combine robust estimation and variable selection method is proposed to simultaneously estimate the parameters and select the...