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Corrected Maximum Likelihood Estimators in Linear Heteroskedastic Regression ModelsCorrected Maximum Likelihood Estimators in Linear Heteroskedastic Regression Models
(Sociedade Brasileira de Econometria, 2008)
Crop yield estimation using satellite images: comparison of linear and non-linear models
(Facultad de Ciencias Agropecuarias., 2018)
Robust estimators in partly linear regression models on Riemannian manifolds
(Taylor, 2014)
Under a partly linear model we study a family of robust estimates for the regression parameter and the regression function when some of the predictors take values on a Riemannian manifold. We obtain the consistency and the ...
On influence diagnostics in elliptical multivariate regression models with equicorrelated random errors
(ELSEVIER SCIENCE BV, 2014)
In this paper we discuss estimation and diagnostic procedures in elliptical multivariate regression models with equicorrelated random errors. Two procedures are proposed for the parameter estimation and the local influence ...
Robust and sparse estimators for linear regression models
(Elsevier Science, 2017-07)
Penalized regression estimators are popular tools for the analysis of sparse and high-dimensional models. However, penalized regression estimators defined using an unbounded loss function can be very sensitive to the ...
Robust tests for linear regression models based on τ-estimates
(Elsevier Science, 2016-01)
ANOVA tests are the standard tests to compare nested linear models fitted by least squares. These tests are equivalent to likelihood ratio tests, so they have high power. However, least squares estimators are very vulnerable ...