The multiple regression model where independent variables are measured unprecisely.
Czapkiewicz, Anna, Dawidowicz, Antoni L. (2005)
Zeszyty Naukowe Uniwersytetu Jagiellońskiego. Universitatis Iagellonicae Acta Mathematica
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Czapkiewicz, Anna, Dawidowicz, Antoni L. (2005)
Zeszyty Naukowe Uniwersytetu Jagiellońskiego. Universitatis Iagellonicae Acta Mathematica
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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.
Atanasiu, Virginia (2008)
APPS. Applied Sciences
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Wong, Wing-Keung, Bian, Guorui (2000)
Journal of Applied Mathematics and Decision Sciences
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Zuzana Prášková, Pavel Vaněček (2011)
Kybernetika
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This work deals with a multivariate random coefficient autoregressive model (RCA) of the first order. A class of modified least-squares estimators of the parameters of the model, originally proposed by Schick for univariate first-order RCA models, is studied under more general conditions. Asymptotic behavior of such estimators is explored, and a lower bound for the asymptotic variance matrix of the estimator of the mean of random coefficient is established. Finite sample properties are...
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...
Ristić, Miroslav, Popović, Biljana Č. (2004)
Novi Sad Journal of Mathematics
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Gabriela Beganu (2007)
RACSAM
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The necessary and sufficient condition for the ordinary least squares estimators (OLSE) to be the best linear unbiased estimators (BLUE) of the expected mean in the general univariate linear regression model was given by Kruskal (1968) using a coordinate-free approach. The purpose of this article is to present in the same manner some alternative forms of this condition and to prove two of the Haberman’s equivalent conditions in a different and simpler way. The results obtained in the...