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Empirical likelihood for quantile regression models with response data missing at random

S. Luo, Shuxia Pang (2017)

Open Mathematics

This paper studies quantile linear regression models with response data missing at random. A quantile empirical-likelihood-based method is proposed firstly to study a quantile linear regression model with response data missing at random. It follows that a class of quantile empirical log-likelihood ratios including quantile empirical likelihood ratio with complete-case data, weighted quantile empirical likelihood ratio and imputed quantile empirical likelihood ratio are defined for the regression...

Estimación bayesiana de una función de fiabilidad con conocimiento a priori gamma expendido.

Domingo Morales, Leandro Pardo, Vicente Quesada (1987)

Trabajos de Investigación Operativa

Se plantea el problema de estimar una función de fiabilidad en el contexto bayesiano no paramétrico, pero utilizando técnicas paramétricas de estimación en procesos estocásticos. Se define el proceso gamma extendido, cuyas trayectorias son tasas de azar crecientes cuando se eligen convenientemente los parámetros del proceso. Se obtienen estimadores basados en este proceso, se estudian sus propiedades asintóticas bayesianas, y se termina con un ejemplo de aplicación mediante simulación.

Estimación no paramétrica de la función de distribución.

Juan Manuel Vilar Fernández (1991)

Qüestiió

Sea X una variable aleatoria con función de distribución F(x) y función de densidad f(x) y X1, X2,..., Xn un conjunto de observaciones de la variable que pueden ser dependientes. Se definen dos estimadores no paramétricos generales (uno recursivo y el otro no recursivo) de la función de distribución.Bajo condiciones aceptables se obtiene el sesgo y la varianza y covarianza asintótica de los estimadores definidos. Finalmente se prueban propiedades de consistencia y normalidad asintótica.

Estimación paramétrica bayesiana no paramétrica de funciones de supervivencia con observaciones parcialmente censuradas.

Domingo Morales, Vicente Quesada, Leandro Pardo (1986)

Trabajos de Estadística

The problem of nonparametric estimation of a survival function based on a partially censored on the right sample is established in a Bayesian context, using parametric Bayesian techniques. Estimates are obtained considering neutral to the right processes, they are particularized to some of them, and their asymptotic properties are studied from a Bayesian point of view. Finally, an application to a Dirichlet process is simulated.

Estimates of the covariance matrix of vectors of u-statistics and confidence regions for vectors of Kendall's tau

František Rublík (2016)

Kybernetika

Consistent estimators of the asymptotic covariance matrix of vectors of U -statistics are used in constructing asymptotic confidence regions for vectors of Kendall’s correlation coefficients corresponding to various pairs of components of a random vector. The regions are products of intervals computed by means of a critical value from multivariate normal distribution. The regularity of the asymptotic covariance matrix of the vector of Kendall’s sample coefficients is proved in the case of sampling...

Estimating a discrete distribution via histogram selection

Nathalie Akakpo (2011)

ESAIM: Probability and Statistics

Our aim is to estimate the joint distribution of a finite sequence of independent categorical variables. We consider the collection of partitions into dyadic intervals and the associated histograms, and we select from the data the best histogram by minimizing a penalized least-squares criterion. The choice of the collection of partitions is inspired from approximation results due to DeVore and Yu. Our estimator satisfies a nonasymptotic oracle-type inequality and adaptivity properties in the minimax...

Estimating a discrete distribution via histogram selection

Nathalie Akakpo (2011)

ESAIM: Probability and Statistics

Our aim is to estimate the joint distribution of a finite sequence of independent categorical variables. We consider the collection of partitions into dyadic intervals and the associated histograms, and we select from the data the best histogram by minimizing a penalized least-squares criterion. The choice of the collection of partitions is inspired from approximation results due to DeVore and Yu. Our estimator satisfies a nonasymptotic oracle-type inequality and adaptivity properties in the minimax...

Estimating composite functions by model selection

Yannick Baraud, Lucien Birgé (2014)

Annales de l'I.H.P. Probabilités et statistiques

We consider the problem of estimating a function s on [ - 1 , 1 ] k for large values of k by looking for some best approximation of s by composite functions of the form g ∘ u . Our solution is based on model selection and leads to a very general approach to solve this problem with respect to many different types of functions g , u and statistical frameworks. In particular, we handle the problems of approximating s by additive functions, single and multiple index models, artificial neural networks, mixtures of Gaussian...

Estimating median and other quantiles in nonparametric models

Ryszard Zieliński (1995)

Applicationes Mathematicae

Though widely accepted, in nonparametric models admitting asymmetric distributions the sample median, if n=2k, may be a poor estimator of the population median. Shortcomings of estimators which are not equivariant are presented.

Estimating quantiles with Linex loss function. Applications to VaR estimation

Ryszard Zieliński (2005)

Applicationes Mathematicae

Sometimes, e.g. in the context of estimating VaR (Value at Risk), underestimating a quantile is less desirable than overestimating it, which suggests measuring the error of estimation by an asymmetric loss function. As a loss function when estimating a parameter θ by an estimator T we take the well known Linex function exp{α(T-θ)} - α(T-θ) - 1. To estimate the quantile of order q ∈ (0,1) of a normal distribution N(μ,σ), we construct an optimal estimator in the class of all estimators of the form...

Estimating the conditional expectations for continuous time stationary processes

Gusztáv Morvai, Benjamin Weiss (2020)

Kybernetika

One of the basic estimation problems for continuous time stationary processes X t , is that of estimating E { X t + β | X s : s ∈ [ 0 , t ] } based on the observation of the single block { X s : s ∈ [ 0 , t ] } when the actual distribution of the process is not known. We will give fairly optimal universal estimates of this type that correspond to the optimal results in the case of discrete time processes.

Estimation in autoregressive model with measurement error

Jérôme Dedecker, Adeline Samson, Marie-Luce Taupin (2014)

ESAIM: Probability and Statistics

Consider an autoregressive model with measurement error: we observe Zi = Xi + εi, where the unobserved Xi is a stationary solution of the autoregressive equation Xi = gθ0(Xi − 1) + ξi. The regression function gθ0 is known up to a finite dimensional parameter θ0 to be estimated. The distributions of ξ1 and X0 are unknown and gθ belongs to a large class of parametric regression functions. The distribution of ε0is completely known. We propose an estimation procedure with a new criterion computed as...

Currently displaying 81 – 100 of 296