Beta-regression model for periodic data with a trend.
Rydlewski, Jerzy P. (2007)
Zeszyty Naukowe Uniwersytetu Jagiellońskiego. Universitatis Iagellonicae Acta Mathematica
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Rydlewski, Jerzy P. (2007)
Zeszyty Naukowe Uniwersytetu Jagiellońskiego. Universitatis Iagellonicae Acta Mathematica
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Cepeda Cuervo, Edilberto, Peláez Andrade, José Manuel (2004)
Revista Colombiana de Estadística
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Hornišová, K. (2004)
Acta Mathematica Universitatis Comenianae. New Series
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Wojciech Niemiro (1995)
Applicationes Mathematicae
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Statistical inference procedures based on least absolute deviations involve estimates of a matrix which plays the role of a multivariate nuisance parameter. To estimate this matrix, we use kernel smoothing. We show consistency and obtain bounds on the rate of convergence.
Wojciech Niemiro (1993)
Applicationes Mathematicae
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We consider the empirical risk function (for iid ’s) under the assumption that f(α,z) is convex with respect to α. Asymptotics of the minimum of is investigated. Tests for linear hypotheses are derived. Our results generalize some of those concerning LAD estimators and related tests.
Jesus Orbe, Vicente Núñez-Antón (2012)
Kybernetika
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This paper proposes a bias reduction of the coefficients' estimator for linear regression models when observations are randomly censored and the error distribution is unknown. The proposed bias correction is applied to the weighted least squares estimator proposed by Stute [28] [W. Stute: Consistent estimation under random censorship when covariables are present. J. Multivariate Anal. 45 (1993), 89-103.], and it is based on model-based bootstrap resampling techniques that also allow...
Mihoc, Ion, Fătu, Cristina–Ioana (2008)
Acta Universitatis Apulensis. Mathematics - Informatics
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Z. Nowak, A. Stachurski (2003)
Control and Cybernetics
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Paul Deheuvels (2011)
Kybernetika
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We consider, in the framework of multidimensional observations, nonparametric functional estimators, which include, as special cases, the Akaike–Parzen–Rosenblatt kernel density estimators ([1, 18, 20]), and the Nadaraya–Watson kernel regression estimators ([16, 22]). We evaluate the sup-norm, over a given set , of the difference between the estimator and a non-random functional centering factor (which reduces to the estimator mean for kernel density estimation). We show that, under...