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Multivariate quantiles and multiple-output regression quantiles：From L1 optimization to halfspace depth
Multivariate quantile quantile regression halfspace depth
2010/3/10
A new multivariate concept of quantile, based on a directional
version of Koenker and Bassett’s traditional regression quantiles, is
introduced for multivariate location and multiple-output regressi...
Discussion of “Multivariate quantiles and multiple-output regression quantiles：From L1 optimization to halfspace depth”
Multivariate quantiles multiple-output regression quantiles L1 optimization halfspace depth
2010/3/10
First I would like to congratulate the authors for developing a new concept
of directional quantile contours. The work will contribute well to the pursuit
of multivariate quantiles. The multiple out...
The Degrees of Freedom of Partial Least Squares Regression
regression model selection Partial Least Squares Degrees of Freedom
2010/3/10
The derivation of statistical properties for Partial Least Squares regression
can be a challenging task. The reason is that the construction of latent compo-
nents from the predictor variables also ...
Bayesian predictive densities for linear regression models under alpha-divergence loss：some results and open problems
shrinkage prior Bayesian predictive density alpha-divergence Stein effect
2010/3/10
This paper considers estimation of the predictive density for
a normal linear model with unknown variance under -divergence loss for
−1 1. We first give a general canonical form for the...
Sparse Regression Learning by Aggregation and Langevin Monte-Carlo
learning regression estimation logistic regression oracle inequalities sparsity prior Langevin Monte-Carlo
2010/3/18
We consider the problem of regression learning for deterministic design and independent random errors.We start by proving a sharp PAC-Bayesian type bound for the exponentially weighted aggregate (EWA)...
Structured，Sparse Regression With Application to HIV Drug Resistance
Sparse Regression Application HIV Drug Resistance
2010/3/10
We introduce a new version of forward stepwise regression. Our
modification finds solutions to regression problems where the selected
predictors appear in a structured pattern, with respect to a pre...
Adaptive asymptotically efficient estimation in heteroscedastic nonparametric regression
asymptotic bounds adaptive estimation efficient estimation het-eroscedastic regression nonparametric regression Pinsker’s constant
2010/3/10
The paper deals with asymptotic properties of the adaptive proce-
dure proposed in the author paper, 2007, for estimating an unknown
nonparametric regression. We prove that this procedure is asympto...
Sharp non-asymptotic oracle inequalities for nonparametric heteroscedastic regression models
Adaptive estimation Heteroscedastic regression Nonasymptoticestimation Nonparametric estimation Oracle inequality
2010/3/10
An adaptive nonparametric estimation procedure is constructed
for heteroscedastic regression when the noise variance depends on the
unknown regression. A non-asymptotic upper bound for a quadratic
...
Functional Regression for General Exponential Families
Functional Regression General Exponential Families
2010/3/9
The paper derives a minimax lower bound for rates of convergence for
an infinite-dimensional parameter in an exponential family model. An estimator
that achieves the optimal rate is constructed by m...
Admissibility of the usual confidence interval in linear regression
admissibility compromise decision theory confidence interval decisiontheory
2010/3/9
Consider a linear regression model with independent and identically normally distributed
random errors. Suppose that the parameter of interest is a specified linear
combination of the regression par...
Conditional least squares estimation in nonstationary nonlinear stochastic regression models
Stochastic nonlinear regression heteroscedasticity nonstation-ary process time series branching process conditional least squares estimator quasi-likelihood estimator
2010/3/9
National Agronomical Research Institute (INRA)
Let {Zn} be a real nonstationary stochastic process such that
E(Zn|Fn−1)a.s.< 1 and E(Z2n |Fn−1)a.s.< 1, where {Fn} is an increas-
ing seq...
Sequentially Updated Residuals and Detection of Stationary Errors in Polynomial Regression Models
Autoregressive unit root Change-point Control chart Nonparametric smoothing Sequential analysis Weighted partial sum process
2010/3/9
The question whether a time series behaves as a random walk or as a stationary
process is an important and delicate problem, particularly arising in financial statistics,
econometrics, and engineeri...
A goodness-of-fit test for parametric and semi-parametric models in multiresponse regression
additive regression bootstrap empirical likelihood goodness of fit infinite-dimensional parameter kernel estimation monotone regression
2010/3/9
We propose an empirical likelihood test that is able to test the goodness of fit of a class of parametric and semi-parametric multiresponse regression models. The class includes as special cases
full...
Post-L1-Penalized Estimators in High-Dimensional Linear Regression Models
Post-L1-Penalized Estimators High-Dimensional Linear Regression Models
2010/3/9
In this paper we study the post-penalized estimator which applies ordinary,
unpenalized linear regression to the model selected by the first step penalized estimators,
typically the LASSO. It is wel...
Workforce analysis using data mining and linear regression to understand HIV/AIDS prevalence patterns
workforce analysis data mining linear regression HIV/AIDS prevalence patterns
2009/9/27
Increased nurse and physician density are associated with improved health outcomes, suggesting that countries aiming to attain the MDGs related to HIV/AIDS would do well to invest in their health work...