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A GENERAL THEORY FOR ORTHOGONAL ARRAY BASED LATIN HYPERCUBE DESIGNS
Functional decomposition Latin hypercube design orthog- onal array statistical property
2016/1/26
Orthogonal array based Latin hypercube designs (LHDs) are popularly adopted for computer experiments. Because of their stratification on multivariate margins in addition to univariate uniformity, the ...
Functional central limit theorems for supercritical superprocesses
Functional central limit theorem supercritical superprocess excursion measures of superprocesses
2016/1/26
In this paper, we establish some functional central limit theorems for a large class of general supercritical superprocesses with spatially dependent branching mechanisms satisfying a second moment co...
Feature Screening for Ultrahigh Dimensional Categorical Data with Applications
Feature Screening Pearson’s Chi-Square Test Screening Consisten- cy Search Engine Marketing
2016/1/26
Ultrahigh dimensional data with both categorical responses and categorical covari-ates are frequently encountered in the analysis of big data, for which feature screening has become an indispensable s...
Optimal Designs for the Proportional Interference Model
Approximate design theory equivalence theorem interference model optimal design proportional mode
2016/1/26
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
2016/1/26
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...
Optimality of Pairwise Blocked Definitive Screening Designs
Blocking Definitive screening design Optimality Generalized minimum aberration Fold-over
2016/1/26
Definitive screening designs are a new class of three-level designs which are shown superior to the classical central composite designs in response surface methodology. They can be constructed by inse...
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...
Testing the Diagonality of a Large Covariance Matrix in a Regression Setting
Bias-Corrected Test Covariance Diagonality Test High Di- mensional Data
2016/1/26
In multivariate analysis, the covariance matrix associated with a set of vari-ables of interest (namely response variables) commonly contains valuable infor-mation about the dataset. When the dimensio...
Shuffle of min’s random variable approximations of bivariate copulas’realization
Copula Shuffle of Min approximation Narrow bounds of copula
2016/1/26
The comonotonicity and countermonotonicity provide intuitive upper and lower depen-dence relationship between random variables. This paper constructs the shuffle of min’s ran-domvariableapproximations...
Spatial Panel Data Models. Oxford Handbook of Panel Data
Spatial Panel Data Model Oxford Handbook Panel Data
2016/1/26
The consideration of interactions among regions or agents has become increasingly important in various fields of economics. In public economics, a state government’s cigarettes tax rate will be influe...
Efficient GMM Estimation of Spatial Dynamic Panel Data Models
Spatial autoregression Dynamic panels Fixed effects Generalized method of moment
2016/1/26
In this paper we derive the asymptotic properties of GMM estimators for the spatial dynamic panel data model with fixed effects when n is large, and T can be large, but small relative to n. The GMM es...
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 ...
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
2016/1/26
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...
Identification of spatial panel Durbin models
Spatial autoregression Durbin regressors Spatial VAR Dynamic panel Fixed e¤ects Random e¤ects
2016/1/26
Identi…cation of a spatial Durbin model is a concern in the spatial econometrics literature. The concern is similar to the identi…cation of the endogenous e¤ect, the contextual e¤ect and correlation e...
Applying the job demands–resources model to migrant workers: Exploring how and when geographical distance increases quit propensity
Applying the job demands–resources model migrant workers Exploring how
2016/1/26
We extend the job demands–resources model to explain how and when rural migrants whoworkfarfrom theirfamilies andprovincial hometownsaremore likely toleave jobs. Through two studies, we found that the...