搜索结果: 1-6 共查到“社会统计学 high dimensional”相关记录6条 . 查询时间(0.158 秒)
High Dimensional Influence Measure
Cook’s distance High dimensional diagnosis Influential obser- vation Lasso Marginal correlations Variable Screening
2016/1/20
Influence diagnosis is important since presence of influential ob-servations could lead to distorted analysis and misleading interpreta-tions. For high dimensional data, it is particularly so, as the ...
Testing Covariates in High Dimensional Regression
Generalized Linear Model High Dimensional Data Hypothe- ses Testing Paid Search Advertising Partial Covariance
2016/1/20
In a high dimensional linear regression model, we propose a new procedure for testing statistical significance of a subset of regression coefficients. Specifically,we employ the partial covariances be...
Sequential Lasso for feature selection with ultra-high dimensional feature space
extended BIC feature selection selection consistency Sequential Lasso
2011/7/19
We propose a novel approach, Sequential Lasso, for feature selection in linear regression models with ultra-high dimensional feature spaces.
High-Dimensional Structure Estimation in Ising Models: Tractable Graph Families
Graphical model selection Ising models Greedy algorithms
2011/7/19
We consider the problem of high-dimensional Ising (graphical) model selection. We propose a simple algorithm for structure estimation based on the thresholding of the empirical conditional mutual info...
Tight conditions for consistent variable selection in high dimensional nonparametric regression
Tight conditions for consistent variable selection high dimensional nonparametric regression
2011/3/23
We address the issue of variable selection in the regression model with very high ambient dimension, i.e., when the number of covariates is very large. The main focus is on the situation where the num...
Compressible Priors for High-dimensional Statistics
linear inverse problem LASSO sparsity sparse regression ridge regression com-pressible prior compressive sensing instance optimality maximum a posteriori high-dimensional statistics order statistics
2011/3/18
We develop a principled way of identifying probability distributions whose independent and identically distributed (iid) realizations are compressible, i.e., can be approximated as sparse. We focus on...