Variable selection for high dimensional Bayesian density estimation: application to human exposure simulation

نویسندگان
چکیده

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Variable selection for high-dimensional Bayesian density estimation: Application to human exposure simulation

Numerous studies have linked ambient air pollution and adverse health outcomes. Most studies of this nature relate outdoor pollution levels measured at a few monitoring stations with counts of health outcomes. Recently, computational methods have been developed to model the distribution of personal exposures, rather than ambient concentration, and then relate the exposure distribution to the he...

متن کامل

Joint high-dimensional Bayesian variable and covariance selection with an application to eQTL analysis.

We describe a Bayesian technique to (a) perform a sparse joint selection of significant predictor variables and significant inverse covariance matrix elements of the response variables in a high-dimensional linear Gaussian sparse seemingly unrelated regression (SSUR) setting and (b) perform an association analysis between the high-dimensional sets of predictors and responses in such a setting. ...

متن کامل

Thresholded Lasso for high dimensional variable selection and statistical estimation ∗

Given n noisy samples with p dimensions, where n ≪ p, we show that the multi-step thresholding procedure based on the Lasso – we call it the Thresholded Lasso, can accurately estimate a sparse vector β ∈ R in a linear model Y = Xβ + ǫ, where Xn×p is a design matrix normalized to have column l2 norm √ n, and ǫ ∼ N(0, σ2In). We show that under the restricted eigenvalue (RE) condition (Bickel-Rito...

متن کامل

Thresholding Procedures for High Dimensional Variable Selection and Statistical Estimation

Given n noisy samples with p dimensions, where n ≪ p, we show that the multistep thresholding procedure can accurately estimate a sparse vector β ∈ R in a linear model, under the restricted eigenvalue conditions (Bickel-Ritov-Tsybakov 09). Thus our conditions for model selection consistency are considerably weaker than what has been achieved in previous works. More importantly, this method allo...

متن کامل

Bayesian Variable Selection in Clustering High-Dimensional Data With Substructure

In this article we focus on clustering techniques recently proposed for highdimensional data that incorporate variable selection and extend them to the modeling of data with a known substructure, such as the structure imposed by an experimental design. Our method essentially approximates the within-group covariance by facilitating clustering without disrupting the groups defined by the experime...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Journal of the Royal Statistical Society: Series C (Applied Statistics)

سال: 2011

ISSN: 0035-9254

DOI: 10.1111/j.1467-9876.2011.01020.x