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This paper provides an overview of control function (CF) methods for solving the problem of endogenous explanatory variables (EEVs) in linear and nonlinear models. CF methods often can be justified in...
The research on the local correlation structure of copula function is an attractive topic.This paper investigates bivariate copula function’s local correlation structure by defining its concentration ...
We consider an optimizing process (or parametric optimization problem), i.e., an optimization problem that depends on some parameters. We present a method for imputing or estimating the objective func...
In this paper, we develop regenerative representations for the covariance function of both a non-delayed regenerative process and a stationary regenerative process. These results are used to obtain co...
We study the estimation of steady-state performance measures from an Ad-valued stochastic process Y = {Y(t): t≥0} representing the output of a simulation. In many applications, we may be interested in...
We examine different ways of numerically computing the distribution function of conditional expectations where the conditioning element takes values in a finite or countably infinite outcome space. Bo...
This is the first of a series of papers which is devoted to a comprehensive theory of maps between orbifolds. In this paper, we define the maps in the more general context of orbispaces, and establish...
In this paper we show how to use Fourier transform methods to analyze the asymptotic behavior of kernel distribution function estimators. Exact expressions for the mean integrated squared error in ter...
We develop the basic building blocks of a frequency domain framework for drawing statistical inferences on the second-order structure of a stationary sequence of functional data. The key element in su...
Bivariate extreme-value distributions have been used in modeling extremes in environmental sciences and risk management. An important issue is estimating the dependence function, such as the Pickands ...
Challenging research in various fields has driven a wide range of methodological advances in variable selection for regression models with high-dimensional predictors. In comparison, selection of nonl...
Challenging research in various fields has driven a wide range of methodological advances in variable selection for regression models with high-dimensional predictors. In comparison, selection of nonl...
The analysis of the joint cumulative distribution function (CDF) with bivariate event time data is a challenging problem both theoretically and numerically. This paper develops a tensor spline-based s...
Evaluating the overall ability of players in the National Hockey League (NHL) is a dicult task. Existing methods such as the famous \plus/minus" statistic have many shortcomings. Standard linear regr...
A commonly used characteristic of statistical dependence of adjacency relations in real networks, the clustering coecient, evaluates chances that two neighbours of a given vertex are adjacent. Anothe...

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