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Nonparametric Regression with Discrete Covariate and Missing Values
Nonparametric Regression Discrete kernel smoothing Imputation Missing Values Variance Reduction
2016/1/19
We consider nonparametric regression with a mixture of continuous and discrete ex-planatory variables where realizations of the response variable may be missing. An impu-tation based nonparametric reg...
Adaptive estimation in nonparametric regression with one-sided errors
adaptive convergence rates non-regular regression frontier estimation bandwidth selection Lepski's method minimax optimality Pickands estimator
2013/6/14
We consider the model of non-regular nonparametric regression where smoothness constraints are imposed on the regression function and the regression errors are assumed to decay with some sharpness lev...
Switching Nonparametric Regression Models and the Motorcycle Data revisited
nonparametric regression machine learning mixture of Gaussian processes latent variables EM algorithm motorcy-cle data
2013/6/14
We propose a methodology to analyze data arising from a curve that, over its domain, switches among J states. We consider a sequence of response variables, where each response y depends on a covariate...
Simultaneous L^2- and L^inf-Adaptation in Nonparametric Regression
Adaptive estimation nonparametric regression thresholding wavelets
2013/4/27
Consider the nonparametric regression framework. It is a classical result that the minimax rates for L^2- and L^inf-risk over a H\"older ball with smoothness index \beta are n^(-\beta/(2\beta+1)) and ...
Spatially-adaptive sensing in nonparametric regression
Nonparametric regression, adaptive sensing sequential design active learning spatial adaptation spatially-inhomogeneous functions.
2012/9/18
While adaptive sensing has provided improved rates of convergence in sparse regression and classi cation, results in nonparametric regres-sion have so far been restricted to quite speci c classes of f...
Nonparametric Regression Estimation with Incomplete Data: Minimax Global Convergence Rates and Adaptivity
Adaptivity Besov spaces inhomogeneous data minimax estimation
2011/7/6
We consider the nonparametric regression estimation problem of recovering an unknown response function $f$ on the basis of incomplete data when the design points follow a known density $g$ with a fini...
Pointwise Adaptive M-estimation in Nonparametric Regression
adaptation Huber function Lepski's method M-estimation minimax estimation nonparametric regression robust estimation pointwise estimation
2011/6/17
This paper deals with the nonparametric estimation in heteroscedastic
regression Yi = f(Xi) + i; i = 1; : : : ; n, with incomplete information,
i.e. each real random variable i has a density gi wh...
Tight conditions for consistent variable selection in high dimensional nonparametric regression
Tight conditions for consistent variable selection high dimensional nonparametric regression
2011/3/23
We address the issue of variable selection in the regression model with very high ambient dimension, i.e., when the number of covariates is very large. The main focus is on the situation where the num...
Tight conditions for consistent variable selection in high dimensional nonparametric regression
variable selection high dimensional nonparametric regression
2011/3/22
We address the issue of variable selection in the regression model with very high ambient dimension, i.e., when the number of covariates is very large. The main focus is on the situation where the nu...
Nonparametric regression with filtered data
censoring counting process theory hazard functions kernel estimation local linear estimation truncation
2011/3/21
We present a general principle for estimating a regression function nonparametrically, allowing for a wide variety of data filtering, for example, repeated left truncation and right censoring. Both th...
Testing Parallelism of Nonparametric Regression Curves
Testing Parallelism Nonparametric Regression Curves
2010/10/19
This paper considers the inference of regression functions in the context of multiple time series. For an arbitrary number of time series observed at a large number of time points, we test the hypoth...
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...
Nonparametric Regression in Proportional Hazards Models
asymptotic distribution censoring time estimation of the first order derivative failure time integration, local partial likelihood, local polynomial fitting nonparametric regression proportional hazards models two-sample problem
2009/3/9
Fan et al. (1997) considered two kinds of nonparametric estimators of the effects of the covariates in proportional hazards models. One of them has no parametric assumption on the baseline hazard func...
Nonparametric denoising Signals of Unknown Local Structure，II：Nonparametric Regression Estimation
Nonparametric denoising adaptive filtering minimax estimation nonparametric regression
2010/3/18
We consider the problem of recovering of continuous multi-dimensional functions f
from the noisy observations over the regular grid m−1Zd, m ∈ N∗. Our focus is at
the adaptive estimation...
MINIMALLY BIASED NONPARAMETRIC REGRESSION AND AUTOREGRESSION
nonparametric regression autoregression Fourier transform
2009/2/25
A nonparametric regression estimator is introduced which adapts to the smoothness
of the unknown function being estimated. This property allows the new estimator
to automatically achieve minimal bia...