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Una aplicación de la estimación no paramétrica al modelo lineal general con varianza no homógenea.

Wenceslao González Manteiga (1985)

Trabajos de Estadística e Investigación Operativa

En este trabajo se introduce un nuevo estimador de la recta de regresión cuando la varianza de los errores aleatorios no es homogénea. La consideración de que la función varianza sea suave nos permite estimarla mediante métodos de estimación no paramétrica para luego a través de tales estimaciones definir un estimador mínimo cuadrático ponderado. Se prueba que tal estimador es asintóticamente optimal en el sentido de la mínima varianza.

Una clase de estimadores para los parámetros de un proceso AR(1), obtenidos a partir de estimaciones no paramétricas previas.

Wenceslao Gonzalez Manteiga, Juan Manuel Vilar Fernández (1987)

Trabajos de Estadística

Sea {Xt}t ∈ Z+ una serie de tiempo estacionaria que sigue el modelo autorregresivo de orden 1: Xt = λ + ρXt-1 + et, siendo {et} variables aleatorias i.i.d. de media cero y varianza σ2; a partir de una muestra del proceso {X1, ..., Xn} se calcula en una primera etapa τ'n, estimador no paramétrico de la función de predicción τ(x) = E[Xt/Xt-1 = x] y Ω'n, estimador no paramétrico de la función de distribución asociada al proceso. Esto nos permite en una segunda etapa calcular estimaciones de los parámetros...

Unbiased group-wise alignment by iterative central tendency estimations

M. S. De Craene, B. Macq, F. Marques, P. Salembier, S. K. Warfield (2008)

Mathematical Modelling of Natural Phenomena

This paper introduces a new approach for the joint alignment of a large collection of segmented images into the same system of coordinates while estimating at the same time an optimal common coordinate system. The atlas resulting from our group-wise alignment algorithm is obtained as the hidden variable of an Expectation-Maximization (EM) estimation. This is achieved by identifying the most consistent label across the collection of images at each voxel in the common frame of coordinates.
In an...

Uniform strong consistency of a frontier estimator using kernel regression on high order moments

Stéphane Girard, Armelle Guillou, Gilles Stupfler (2014)

ESAIM: Probability and Statistics

We consider the high order moments estimator of the frontier of a random pair, introduced by [S. Girard, A. Guillou and G. Stupfler, J. Multivariate Anal. 116 (2013) 172–189]. In the present paper, we show that this estimator is strongly uniformly consistent on compact sets and its rate of convergence is given when the conditional cumulative distribution function belongs to the Hall class of distribution functions.

Using auxiliary information in statistical function estimation

Sergey Tarima, Dmitri Pavlov (2006)

ESAIM: Probability and Statistics

In many practical situations sample sizes are not sufficiently large and estimators based on such samples may not be satisfactory in terms of their variances. At the same time it is not unusual that some auxiliary information about the parameters of interest is available. This paper considers a method of using auxiliary information for improving properties of the estimators based on a current sample only. In particular, it is assumed that the information is available as a number of estimates based...

Using auxiliary information in statistical function estimation

Sergey Tarima, Dmitri Pavlov (2005)

ESAIM: Probability and Statistics

In many practical situations sample sizes are not sufficiently large and estimators based on such samples may not be satisfactory in terms of their variances. At the same time it is not unusual that some auxiliary information about the parameters of interest is available. This paper considers a method of using auxiliary information for improving properties of the estimators based on a current sample only. In particular, it is assumed that the information is available as a number of estimates based...

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