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The random design setting for linear regression concerns estimators based on a random sample of covariate/response pairs.
Some results on random design regression with long memory errors and predictors
memory errors predictors random design
2011/3/24
This paper studies nonparametric regression with long memory (LRD) errors and predictors. First, we formulate general conditions which guarantee the standard rate of convergence for a nonparametric ke...
ADAPTIVE KERNEL ESTIMATION OF THE MODE IN A NONPARAMETRIC RANDOM DESIGN REGRESSION MODEL
Nonparametric regression random design mode kernel smoothing Nadaraya-Watson estimator
2009/9/21
In a nonparametric regession model with random design,
where the regression function m is given by rn (x). = E (Y I X = x),
estimation of the location 0 (mode) and size m(B) of a unique maximum
of ...
Wavelet block thresholding for samples with random design: A minimax approach under the Lp risk
Regression with random design wavelets block thresholding
2009/9/18
We consider the regression model with (known) random design. We investigate the minimax performances of an adaptive wavelet block thresholding estimator under the Lp risk with p >2 over Besov balls. W...