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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...
Maximum-Likelihood Estimation For Diffusion Processes Via Closed-Form Density Expansions
asymptotic expansion diffusion discrete observation maximum-likelihood estimation transition density
2016/1/25
This paper proposes a widely applicable method of approximate maximum-likelihood estimation for multivariate diffusion process from discretely sampled data. A closed-form asymptotic expansion for tran...
Testing Additive Separability of Error Term in Nonparametric Structural Models
Additive Separability Hypotheses Testing Nonparametric Structural Equation Non- separable Models
2016/1/25
This paper considers testing additive error structure in nonparametric structural models, against the alternative hypothesis that the random error term enters the nonparametric model non-additively.We...
On Implied Volatility for Options – Some Reasons to Smile and More to Correct
Bias correction Implied volatility,Kernel estimator Pricing errors
2016/1/25
We analyze the properties of the implied volatility, the commonly used volatility estimator by direct option price inversion. It is found that the implied volatility is subject to a systematic bias in...
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...
Multivariate Regression Shrinkage and Selection by Canonical Correlation Analysis
Adaptive Lasso Canonical Correlation Analysis Multivariate Regression
2016/1/25
The problem of regression shrinkage and selection for multivariate regression is considered. The goal is to consistently identify those variables relevant for regression. This is done not only for pre...
Preconditioning is a technique from numerical linear algebra that can accelerate algorithms to solve systems of equations. In this pa-per, we demonstrate how preconditioning can circumvent a stringent...
Preconditioning to Comply with the Irrepresentable Conditio
Preconditioning Lasso Sign consistency
2016/1/25
Preconditioning is a technique from numerical linear algebra that can accelerate algorithms to solve systems of equations. In this pa-per, we demonstrate how preconditioning can circumvent a stringent...
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...
Bounds for the Sum of Dependent Risks and Worst Value-at-Risk with Monotone Marginal Densities
Complete mixability Monotone density Sum of dependent risks Value-at- Risk
2016/1/25
In quantitative risk management, it is important and challenging to find sharp bounds for the distribution of the sum of dependent risks with given marginal distributions, but an unspecified dependenc...
Compressive Network Analysis
network data analysis compressive sensing Radon basis pursuit restricted isometry property clique detection
2016/1/25
Modern data acquisition routinely produces massive amounts of network data.Though many methods and models have been proposed to analyze such data, the research of network data is largely disconnected ...
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...
Optimal Designs for the Proportional Interference Model
Approximate design theory equivalence theorem interference model optimal design proportional model
2016/1/20
The interference model has been widely used and studied in block experiments where the treatment for a particular plot has effects on its neighbor plots. In this paper, we study optimal circular desig...
Estimating Mixture of Gaussian Processes by Kernel Smoothing
Identifiability EM algorithm Kernel regression Gaussian process Functional principal component analysis
2016/1/20
When the functional data are not homogeneous, e.g., there exist multiple classes of func-tional curves in the dataset, traditional estimation methods may fail. In this paper, we propose a new estimati...
Estimation of Spatial Panel Data Models with Time Varying Spatial Weights Matrices
Spatial autoregression Panel data Time varying spatial weights matrices Fixed e¤ects Maximum likelihood Impact analysis
2016/1/20
This paper investigates the quasi-maximum likelihood (QML) estimation of spatial panel data models where spatial weights matrices can be time varying. We show that QML estimate is consistent and asymp...