Adaptive Monte Carlo for Binary Regression with Many Regressors
نویسندگان
چکیده
This article describes a method for efficient posterior simulation for Bayesian variable selection in probit regression models with many regressors but few observations. A proposal on model space is described which contains a tuneable parameter. An adaptive approach to choosing this tuning parameter is described which allows automatic, efficient computation in these models. The methods is applied to the analysis of gene expression data.
منابع مشابه
Adaptive Monte Carlo for Bayesian Variable Selection in Regression Models
This article describes a method for efficient posterior simulation for Bayesian variable selection in Generalized Linear Models with many regressors but few observations. A proposal on model space is described which contains a tuneable parameter. An adaptive approach to choosing this tuning parameter is described which allows automatic, efficient computation in these models. The method is appli...
متن کاملNon-parametric regression for binary dependent variables
Finite-sample properties of non-parametric regression for binary dependent variables are analyzed. Non parametric regression is generally considered as highly variable in small samples when the number of regressors is large. In binary choice models, however, it may be more reliable since its variance is bounded. The precision in estimating conditional means as well as marginal effects is invest...
متن کاملMultivariate Bayesian variable selection and prediction
The multivariate regression model is considered with p regressors. A latent vector with p binary entries serves to identify one of two types of regression coef®cients: those close to 0 and those not. Specializing our general distributional setting to the linear model with Gaussian errors and using natural conjugate prior distributions, we derive the marginal posterior distribution of the binary...
متن کاملComparison of Regressor Selection Methods in System Identification, Report no. LiTH-ISY-R-2730
In non-linear system identification the set of non-linear models is very rich and the number of parameters usually grows very rapidly with the number of regressors. In order to reduce the large variety of possible models as well as the number of parameters, it is of great interest to exclude irrelevant regressors before estimating any model. In this work, three existing approaches for regressor...
متن کاملSequential Monte Carlo on large binary sampling spaces
A Monte Carlo algorithm is said to be adaptive if it automatically calibrates its current proposal distribution using past simulations. The choice of the parametric family that defines the set of proposal distributions is critical for good performance. In this paper, we present such a parametric family for adaptive sampling on high-dimensional binary spaces. A practical motivation for this prob...
متن کامل