搜索结果: 1-15 共查到“统计学 screening”相关记录17条 . 查询时间(0.112 秒)
Feature Screening for Ultrahigh Dimensional Categorical Data with Applications
Feature Screening Pearson’s Chi-Square Test Screening Consisten- cy Search Engine Marketing
2016/1/26
Ultrahigh dimensional data with both categorical responses and categorical covari-ates are frequently encountered in the analysis of big data, for which feature screening has become an indispensable s...
Optimality of Pairwise Blocked Definitive Screening Designs
Blocking Definitive screening design Optimality Generalized minimum aberration Fold-over
2016/1/26
Definitive screening designs are a new class of three-level designs which are shown superior to the classical central composite designs in response surface methodology. They can be constructed by inse...
Marginal empirical likelihood and sure independence screening
Empirical likelihood high dimensional data analysis independence sure screening large deviation
2016/1/25
We study a marginal empirical likelihood approach in scenarios when the num-ber of variables grows exponentially with the sample size. The marginal empirical likelihood ratios as functions of the para...
Feature Screening for Ultrahigh Dimensional Categorical Data with Applications
Feature Screening Pearson’s Chi-Square Test Screening Consisten- cy Search Engine Marketing Text Classification Ultrahigh Dimensional Data
2016/1/20
Ultrahigh dimensional data with both categorical responses and categorical covari-ates are frequently encountered in the analysis of big data, for which feature screening has become an indispensable s...
Optimality of Pairwise Blocked Definitive Screening Designs
Blocking Definitive screening design Optimality Generalized minimum aberration Fold-over
2016/1/20
Definitive screening designs are a new class of three-level designs which are shown superior to the classical central composite designs in response surface methodology. They can be constructed by inse...
Marginal empirical likelihood and sure independence screening
Empirical likelihood high dimensional data analysis independence sure screening large deviation
2016/1/20
We study a marginal empirical likelihood approach in scenarios when the num-ber of variables grows exponentially with the sample size. The marginal empirical likelihood ratios as functions of the para...
Profiled Forward Regression for Ultrahigh Dimensional Variable Screening in Semiparametric Partially Linear Models
Forward Regression Partially Linear Model Profiled Forward Regres- 9 sion Screening Consistency
2016/1/19
Profiled Forward Regression for Ultrahigh Dimensional Variable Screening in Semiparametric Partially Linear Models.
Reducing decision errors in the paired comparison of the diagnostic accuracy of screening tests with Gaussian outcomes
cancer screening differential verification bias area under the curve type I error power paired screening trial receiver operating characteristic analysis
2013/6/14
Scientists often use a paired comparison of the areas under the receiver operating characteristic curves to decide which continuous cancer screening test has the best diagnostic accuracy. In the paire...
Predictive Correlation Screening: Application to Two-stage Predictor Design in High Dimension
Predictive Correlation Screening Application Two-stage Predictor Design High Dimension
2013/4/27
We introduce a new approach to variable selection, called Predictive Correlation Screening, for predictor design. Predictive Correlation Screening (PCS) implements false positive control on the select...
Nonparametric Independence Screening in Sparse Ultra-High Dimensional Varying Coefficient Models
Sure independence screening Variable selection Sparsity Conditional permutation False posi-tive rates
2013/4/27
The varying-coefficient model is an important nonparametric statistical model that allows us to examine how the effects of covariates vary with exposure variables. When the number of covariates is big...
Nonparametric Independence Screening in Sparse Ultra-High Dimensional Varying Coefficient Models
Sure independence screening Variable selection Sparsity Conditional permutation False posi-tive rates
2013/4/27
The varying-coefficient model is an important nonparametric statistical model that allows us to examine how the effects of covariates vary with exposure variables. When the number of covariates is big...
Parameter-Free High-Dimensional Screening Using Multiple Grouping of Variables
Parameter-Free High-Dimensional Screening Multiple Grouping Variables
2012/9/17
Screening is the problem of estimating a superset of the set of non-zero entries in an unknownp-dimensional vector β given nnoisy observations. In the high-dimensional regime, where p > n, screening a...
Independent screening for single-index hazard rate models with ultra-high dimensional features
screening univariate regression models generalized linear models single-index
2011/6/17
In data sets with many more features than observations, independent screening based on all
univariate regression models leads to a computationally convenient variable selection method.
Recent effort...
Large Scale Correlation Screening
High dimensional inference Variable selection Phase transition Poisson limit Renyi entropy Thresholding Sparsity False discovery
2011/3/18
This paper treats the problem of screening for variables with high correlations in high dimensional data in which there can be many fewer samples than variables. We focus on threshold-based correlatio...
Screening and metamodeling of computer experiments with functional outputs. Application to thermal-hydraulic computations
Functional output Pressurized thermal shock transient
2010/10/19
To perform uncertainty, sensitivity or optimization analysis on scalar variables calculated by a cpu time expensive computer code, a widely accepted methodology consists in first identifying the most...