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Testing the statistical significance of an ultra-high-dimensional naïve Bayes classfier
Binary Predictor Hypothesis Testing Na?ve Bayes Supervised Learning
2016/1/25
The na?ve Bayes approach is one of the most popular methods used for classi?cation. Nevertheless, how to test its statistical signi?cance under an ultra-high-dimensional (UHD) setup is not well unders...
Testing the statistical significance of an ultra-high-dimensional naïve Bayes classfier
Binary Predictor Hypothesis Testing Na?ve Bayes Supervised Learning
2016/1/20
The na?ve Bayes approach is one of the most popular methods used for classi?cation. Nevertheless, how to test its statistical signi?cance under an ultra-high-dimensional(UHD) setup is not well underst...
Factor profiling for ultra high dimensional variable selection
Bayesian Information Criterion Factor Profiling Forward Re- gression Maximum Eigenvalue Ratio Criterion Profiled Independent Screening
2016/1/19
We propose here a novel method of factor profiling (FP) for ultra high dimen-sional variable selection. The new method assumes that the correlation structure of the high dimensional data can be well r...
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...
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.
Extended BIC for linear regression models with diverging number of relevant features and high or ultra-high feature spaces
Diverging number of parameters Feature selection
2011/7/19
In many conventional scientific investigations with high or ultra-high dimensional feature spaces, the relevant features, though sparse, are large in number compared with classical statistical problem...
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...