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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...
On asymptotically optimal confidence regions and tests for high-dimensional models
asymptotically optimal confidence regions tests for high-dimensional models
2013/4/27
We propose a general method for constructing confidence intervals and statistical tests for single or low-dimensional components of a large parameter vector in a high-dimensional model. It can be easi...
The Lasso for High-Dimensional Regression with a Possible Change-Point
Lasso oracleine qualities sample splitting sparsity threshold models
2012/11/23
We consider a high-dimensional regression model with a possible change-point due to a covariate threshold and develop the Lasso estimator of regression coefficients as well as the threshold parameter....
Residual variance and the signal-to-noise ratio in high-dimensional linear models
Asymptoticnormality,high-dimensionaldataanalysis Poincar!a inequality randommatrices residualvariance signal-to-noiseratio
2012/11/21
Residual variance and the signal-to-noise ratio are important quantities in many statistical models and model fitting procedures. They play an important role in regression diagnostics, in determining ...
Modelling interactions in high-dimensional data with Backtracking
Backtracking interactions Lasso parallel computing path algorithm.
2012/9/17
We study the problem of high-dimensional regression when there may be interacting vari-ables. We introduce a new idea called Backtracking, that can be incorporated into many existing high-dimensional ...
Changepoint detection for high-dimensional time series with missing data
Change point detection high-dimensional time series missing data
2012/9/17
This paper describes a novel approach to changepoint detection when the observed high-dimensional data may have missing elements. The performance of classical methods for changepoint detection typical...
Penalized estimation in high-dimensional hidden Markov models with state-specific graphical models
HMM Graphical Lasso Universal Regularization Model Selection MMDL Greedy Backwards Pruning Genome Biology Chromatin Modeling
2012/9/17
We consider penalized estimation in hidden Markov models (HMMs) with multi-variate Normal observations. In the moderate-to-large dimensional setting, estimation for HMMs remains challenging in practic...
Test for bandedness of high-dimensional covariance matrices and bandwidth estimation
Banded covariance matrix bandwidth estimation high data dimension largep, small n nonparametric.
2012/9/17
Motivated by the latest effort to employ banded matrices to esti-mate a high-dimensional covariance Σ, we propose a test for Σbeing banded with possible diverging bandwidth. The test is adaptive to th...
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...
An Improved Data Assimilation Scheme for High Dimensional Nonlinear Systems
Bayesian Estimation Ensemble Data Assimilation Gaussian Sum Expansion Environmental Control
2012/9/17
Nonlinear/non-Gaussian ltering has broad applications in many areas of life sciences where either the dynamic is nonlinear and/or the probability density function of un-certain state is non-Gaussian....
Two-step estimation of high dimensional additive models
additive model group Lasso penalized least squares.
2012/9/19
This paper investigates the two-step estimation of a high dimensional additive regression model, in which the number of nonparametric additive components is potentially larger than the sample size but...
Finite sample posterior concentration in high-dimensional regression
asymptotics Bayesian compressible prior high-dimensional posterior contraction regression shrinkage prior.
2012/9/19
We study the behavior of the posterior distribution in ultra high-dimensional Bayesian Gaussian linear regression models havingp佲n,withpthe number of predictors and nthe sample size. In particular, ou...
Grouping Strategies and Thresholding for High Dimensional Linear Models
Structured sparsity Grouping, Learning Theory Non Linear Methods Block-thresholding coherence Wavelets
2012/9/19
The estimation problem in a high regression model with structured sparsity is investigated.An algorithm using a two steps block thresholding procedure called GR-LOL is provided.Convergence rates are p...
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...