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High Dimensional Generalized Empirical Likelihood for Moment Restrictions with Dependent Data
Generalized empirical likelihood High dimensionality Penalized likelihood Variable selec- tion
2016/1/26
This paper considers the maximum generalized empirical likelihood (GEL) estimation and inference on parameters identified by high dimensional moment restrictions with weakly dependent data when the di...
Testing Covariates in High Dimensional Regression
Generalized Linear Model High Dimensional Data Hypothe- ses Testing
2016/1/26
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...
High Dimensional Stochastic Regression with Latent Factors, Endogeneity and Nonlinearity
α-mixing dimension reduction instrument variables nonstationarity time series
2016/1/26
We consider a multivariate time series model which represents a high dimensional vector process as a sum of three terms: a linear regression of some observed regressors, a linear com-bination of some ...
Tests for High Dimensional Generalized Linear Models
Generalized Linear Model Gene-Sets High Dimensional Covariate Nuisance Parameter U-statistics
2016/1/26
We consider testing regression coefficients in high dimensional generalized linear mod-els. By modifying a test statistic proposed by Goeman et al. (2011) for large but fixed dimensional settings, we ...
Band Width Selection for High Dimensional Covariance Matrix Estimation
Bandable covariance Banding estimator Large p, small n Ratio- consistency Tapering estimator Thresholding estimator
2016/1/25
The banding estimator of Bickel and Levina (2008a) and its tapering version of Cai, Zhang and Zhou (2010), are important high dimensional covariance esti-mators. Both estimators require choosing a ban...
High dimensional stochastic regression with latent factors, endogeneity and nonlinearity
α-mixing dimension reduction instrument variables nonstationarity time series
2016/1/25
We consider a multivariate time series model which represents a high dimensional vector process as a sum of three terms: a linear regression of some observed regressors,a linear combination of some la...
Testing Covariates in High Dimensional Regression
Generalized Linear Model High Dimensional Data Hypothe- ses Testing
2016/1/25
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...
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...
Test for Bandedness of High-Dimensional Covariance Matrices and Bandwidth Estimation
Banded covariance matrix Bandwidth estimation High data dimension Large p small n Nonparametric
2016/1/25
Motivated by the latest effort to employ banded matrices to esti-mate a high-dimensional covariance Σ, we propose a test for Σ being banded with possible diverging bandwidth. The test is adaptive to t...
High Dimensional Generalized Empirical Likelihood for Moment Restrictions with Dependent Data
Generalized empirical likelihood High dimensionality Penalized likelihood
2016/1/20
This paper considers the maximum generalized empirical likelihood (GEL) estimation and inference on parameters identified by high dimensional moment restrictions with weakly dependent data when the di...
Tests for High Dimensional Generalized Linear Models
Generalized Linear Model Gene-Sets High Dimensional Covariate Nuisance Parameter U-statistics
2016/1/20
We consider testing regression coefficients in high dimensional generalized linear mod-els. By modifying a test statistic proposed by Goeman et al. (2011) for large but fixed dimensional settings, we ...
Band Width Selection for High Dimensional Covariance Matrix Estimation
Bandable covariance Banding estimator Large p small n
2016/1/20
The banding estimator of Bickel and Levina (2008a) and its tapering version of Cai, Zhang and Zhou (2010), are important high dimensional covariance esti-mators. Both estimators require choosing a ban...
Tests atternative to higher criticism for high dimensional means under sparsity and column-wise dependence
Large deviation Large p, small n Optimal detection boundary Sparse signal Thresholding Weak dependence
2016/1/20
We consider two alternative tests to the Higher Criticism test of Donoho and Jin (2004) for high dimensional means under the spar-sity of the non-zero means for sub-Gaussian distributed data with unkn...
High dimensional stochastic regression with latent factors, endogeneity and nonlinearity
α-mixing, dimension reduction instrument variables nonstationarity time series
2016/1/20
We consider a multivariate time series model which represents a high dimensional vector process as a sum of three terms: a linear regression of some observed regressors,a linear combination of some la...
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 ...