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We present a joint copula-based model for insurance claims and sizes. It uses bivariate copulae to accommodate for the dependence between these quantities. We derive the general distribution of the po...
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....
We develop algorithms for performing semiparametric regression analysis in real time, with data processed as it is collected and made immediately available via modern telecommunications technologies. ...
The problem of constructing confidence sets in the high dimensional linear model with $n$ response variables and $p$ parameters, possibly $p \ge n$, is considered. Necessary and sufficient conditions ...
Quantile regression predicts the $\tau$-quantile of the conditional distribution of a response variable given the explanatory variable for $\tau\in(0,1)$. The aim of this paper is to establish the asy...
This paper considers the nonparametric regression model with an additive error that is dependent on the explanatory variables. As is common in empirical studies in epidemiology and economics, it also ...
Consider a nonlinear regression model :yi =g(xi, 兤) +ei, i = 1, ..., n,where thexi are random predictorsxi and兤is the unknown parameter vector ranging in a set set 儲伡Rp. All known results on the consi...
We develop a highly scalable optimization method called “hierarchical group-thresholding”for solving a multi-task regression model with complex structured sparsity constraints on both input and output...
Quantile regression is a technique to estimate conditional quantile curves. It pro-vides a comprehensive picture of a response contingent on explanatory variables. In a exible modeling framework, a sp...
We consider quantile regression processes from censored data under dependent data structures and derive a uniform Bahadur representation for those processes. We also consider cases where the dimension...
We consider parameter estimation, hypothesis testing and vari-able selection for partially time-varying coefficient models. Our asymp-totic theory has the useful feature that it can allow dependent, n...
In this thesis we study adaptive nonparametric regression with noise misspecifi-cation and the complexity of approximation of random fields in dependence of the dimension. First, we consider the prob...
We consider the problem of testing a particular type of composite null hypothesis under a nonparametric multivariate regression model. For a given quadraticfunctional Q, the null hypothesis states tha...
Like mean, quantile and variance, mode is also an important measure of central tendency and data summary. Many practical questions often focus on “Which element (gene or file or signal) occurs most of...
Freedman [Adv. in Appl. Math.40(2008) 180–193; Ann. Appl.Stat.2(2008) 176–196] critiqued ordinary least squares regression ad-justment of estimated treatment effects in randomized experiments,using Ne...

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