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Correlated variables in regression: clustering and sparse estimation
Canonical correlation group Lasso Hierarchical clustering High-dimensional inference Lasso Oracle inequality Variable screening Variable selection
2012/11/23
We consider estimation in a high-dimensional linear model with strongly correlated variables. We propose to cluster the variables first and do subsequent sparse estimation such as the Lasso for cluste...
Total loss estimation using copula-based regression models
dependence modeling generalized linear model number of claims claim size policy loss
2012/11/23
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...
The Lasso for High-Dimensional Regression with a Possible Change-Point
Lasso oracleine qualities sample splitting sparsity threshold models
2012/11/23
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....
Real-time semiparametric regression
Approximate Bayesian inference Generalized additive models Meaneld vari-ational Bayes Mixed models Online variational Bayes Penalized splines Wavelets
2012/11/22
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. ...
Confidence Sets in Sparse Regression
composite testing problem high-dimensional inference detection boundary
2012/11/22
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 ...
Asymptotics for penalized spline estimators in quantile regression
Asymptotic normality, B-spline,Penalized spline,Quantile regression
2012/11/22
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...
Nonparametric instrumental regression with non-convex constraints
Nonparametric instrumental regression non-convex constraints
2012/11/21
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 ...
Consistency of M estimates for separable nonlinear regression models
Nonlinear regression separable models con-sistency robust estimation.
2012/9/18
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...
Efficient Algorithm for Extremely Large Multi-task Regression with Massive Structured Sparsity
Algorithm Large Multi-task Regression Massive Structured Sparsity
2012/9/17
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...
Local Quantile Regression
local MLE excess bound propagation condition adaptive bandwidth selection.
2012/9/18
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...
Censored quantile regression processes under dependence and penalization
quantile regression Bahadur representation variable selection weak convergence censored data dependent data
2012/9/18
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...
Inference of time-varying regression models
Information criterion locally stationary processes nonpara-metric hypothesis testings time-varying coefficient models variable selection.
2012/9/17
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...
Adaptive estimation in regression and complexity of approximation of random fields
regression and complexity approximation random fields
2012/9/17
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...
Minimax testing of a composite null hypothesis defined via a quadratic functional in the model of regression
Nonparametric hypotheses testing sharp asymptotics separation rates minimax approach high-dimensional regression.
2012/9/17
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...
Bayesian Mode Regression
Bayesian inference empirical likelihood Markov Chain Monte Carlo methods mode
2012/9/17
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...