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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...
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...
Weak extinction versus global exponential growth of total mass for superdi usions corresponding to the operator Lu + βu − ku2
superdiffusion superprocess measure-valued process gauge theorem Kato class growth bound
2016/1/25
Consider a superdiffusion X on R d corresponding to the semilinear operator A(u) = Lu + βu − ku 2 , where L is a second order elliptic operator, β(·) is in the Kato class and bounded from above,...
Small Value Probabilities for Supercritical Branching Processes with Immigration
Supercritical Galton-Watson branching process small value probability immigration
2016/1/25
We consider a supercritical Galton-Watson branching process with immigration. It is well known that under suitable conditions on the offspring and immigration distributions, there is a finite,strictly...
On Pattern Recovery of The Fused Lasso
Confidence interval jackknife empirical likelihood risk measure
2016/1/25
Quantifying risks is of importance in insurance. In this paper, we employ the jackknife empirical likelihood method to construct confidence intervals for some risk measures and related quantities stud...
Jackknife Empirical Likelihood Method for Some Risk Measures and Related Quantities
Confidence interval jackknife empirical likelihood risk measure
2016/1/25
Quantifying risks is of importance in insurance. In this paper, we employ the jackknife empirical likelihood method to construct confidence intervals for some risk measures and related quantities stud...
Balanced Incomplete Latin Square Designs
Balanced incomplete Latin square information matrix optimality orthogonal Latin square
2016/1/25
Latin squares have been widely used to design an experiment where the blocking factors and treatment factors are of the same levels. For some experiments, the size of blocks may be less than the numbe...
Bessel Processes, Stochastic Volatility, and Timer Options
Bessel Processes Stochastic Volatility Timer Options
2016/1/25
Motivated by analytical valuation of timer options (an important innovation in realized variance based derivatives), we explore their novel mathematical connection with stochastic volatility and Besse...
Estimation in semiparametric models with missing data
Copulas imputation kernel smoothing missing at random nuisance function partially linear model
2016/1/25
We propose a novel varying coefficient model, called princi-pal varying coefficient model (PVCM), by characterizing the varying coeffi-cients through linear combinations of a few principal functions. ...
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...