Results 71 to 80 of about 92 (91)
Learning from dependent observations
In most papers establishing consistency for learning algorithms it is assumed that the observations used for training are realizations of an i.i.d. process. In this paper we go far beyond this classical framework by showing that support vector machines (
Scovel, Clint +2 more
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Local linear regression for functional predictor and scalar response
The aim of this work is to introduce a new nonparametric regression technique in the context of functional covariate and scalar response. We propose a local linear regression estimator and study its asymptotic behaviour.
Grané, Aurea, Baíllo, Amparo
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CLASSIFICATION WITH A REJECT OPTION USING A HINGE LOSS
, 2008 . We consider the problem of binary classification where the classifier can, for a particular cost, choose not to classify an observation. Just as in the conventional classification problem, minimization of the sample average of the cost is a difficult ...
H. Wegkamp, Peter L. Bartlett, Marten
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Nonparametric lack-of-fit tests for parametric mean-regression models with censored data
We developed two kernel smoothing based tests of a parametric mean-regression model against a nonparametric alternative when the response variable is right-censored.
Patilea, V., Lopez, O.
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Model checking in errors-in-variables regression
This paper discusses a class of minimum distance tests for fitting a parametric regression model to a class of regression functions in the errors-in-variables model.
Song, Weixing
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A refined Jensen's inequality in Hilbert spaces and empirical approximations
Let be a convex mapping and a Hilbert space. In this paper we prove the following refinement of Jensen's inequality: for every A,B such that and B[subset of]A.
Leorato, S.
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A note on the Bayes factor in a semiparametric regression model
In this paper, we consider a semiparametric regression model where the unknown regression function is the sum of parametric and nonparametric parts. The parametric part is a finite-dimensional multiple regression function whereas the nonparametric part ...
Choi, Taeryon +2 more
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Recent sufficient dimension reduction methodologies in multivariate regression do not have direct application to a categorical predictor. For this, we define the multivariate central partial mean subspace and propose two methodologies to estimate it. The
Yoo, Jae Keun
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Analysis of correlated binary data under partially linear single-index logistic models
Clustered data arise commonly in practice and it is often of interest to estimate the mean response parameters as well as the association parameters. However, most research has been directed to address the mean response parameters with the association ...
Liang, Hua, Yi, Grace Y., He, Wenqing
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Flexible modeling based on copulas in nonparametric median regression
Consider the model Y=m(X)+[epsilon], where m([dot operator])=med(Y[dot operator]) is unknown but smooth. It is often assumed that [epsilon] and X are independent. However, in practice this assumption is violated in many cases.
Braekers, Roel, Van Keilegom, Ingrid
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