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Testing in generalized partly linear models: A robust approach
(Elsevier, 2013-01)
In this paper, we introduce a family of robust statistics which allow to decide between a parametric model and a semiparametric one. More precisely, under a generalized partially linear model, i.e., when the observations ...
Robust inference in generalized partially linear models
(Elsevier Science, 2010-12)
In many situations, data follow a generalized partly linear model in which the mean of the responses is modeled, through a link function, linearly on some covariates and nonparametrically on the remaining ones. A new class ...
Bayesian modeling of autoregressive partial linear models with scale mixture of normal errors
(Taylor & Francis LtdAbingdonInglaterra, 2013)
Robust Estimates in Generalized Partially Linear Single-Index Models
(Springer, 2012-06)
A natural generalization of the well known generalized linear models is to allow only for some of the predictors to be modeled linearly while others are modeled nonparametrically. However, this model can face the so called ...
Robust estimators in a generalized partly linear regression model under monotony constraints
(Springer, 2019-02)
In this paper, we consider the situation in which the observations follow an isotonic generalized partly linear model. Under this model, the mean of the responses is modelled, through a link function, linearly on some ...
A class of mixtures of dependent tail-free processes
(OXFORD UNIV PRESS, 2011)
We propose a class of dependent processes in which density shape is regressed on one or more predictors through conditional tail-free probabilities by using transformed Gaussian processes. A particular linear version of ...
The parametric and additive partial linear regressions based on the generalized odd log-logistic log-normal distribution
(Taylor & Francis Inc, 2020-07-19)
We propose two new regressions based on the generalized odd log-logistic log-normal distribution allowing for positive and negative skewness to model bimodal data. The first one is the parametric regression and the second ...
Higher-Order Partial Least Squares (HOPLS) : a generalized multi-linear regression method
(IEEE Computer Society, 2013-07)
A new generalized multilinear regression model, termed the Higher-Order Partial Least Squares (HOPLS), is introduced with the aim to predict a tensor (multiway array) Y from a tensor X through projecting the data onto the ...