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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...
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...
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...
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...
Test for Bandedness of High-Dimensional Covariance Matrices and Bandwidth Estimation
Banded covariance matrix Bandwidth estimation High data dimension Large p small n Nonparametric
2016/1/20
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...
EigenPrism:Inference for High-Dimensional Signal-to-Noise Ratios
EigenPrism High-Dimensional Signal Noise Ratios
2015/6/17
Consider the following three important problems in statistical inference, namely, constructing confidence intervals for (1) the error of a high-dimensional (p > n) regression estimator, (2) the linear...
Supplementary Appendix for "Inference on Treatment Effects After Selection Amongst High-Dimensional Controls"
Supplementary Appendix "Inference on Treatment Effects After Selection Amongst High-Dimensional Controls"
2013/6/14
In this supplementary appendix we provide additional results, omitted proofs and extensive simulations that complement the analysis of the main text
Regularity Properties of High-dimensional Covariate Matrices
high-dimensional regression instrumental variables sparse estimation compressed sensing random matrix re-stricted eigenvalue compatibility,ℓ q sensitivity computational complex-ity NP-hardness
2013/6/14
Regularity properties such as the incoherence condition, the restricted isometry property, compatibility, restricted eigenvalue and $\ell_q$ sensitivity of covariate matrices play a pivotal role in hi...