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Empirical regression quantile processes

Jana Jurečková, Jan Picek, Martin Schindler (2020)

Applications of Mathematics

We address the problem of estimating quantile-based statistical functionals, when the measured or controlled entities depend on exogenous variables which are not under our control. As a suitable tool we propose the empirical process of the average regression quantiles. It partially masks the effect of covariates and has other properties convenient for applications, e.g. for coherent risk measures of various types in the situations with covariates.

Estimación de la función cuantil y cuantildensidad mediante polinomios de Kantorovic.

Ana Fernández Palacín, José Muñoz Pérez (1990)

Trabajos de Estadística

En este trabajo se propone un estimador para la función cuantil, basado en polinomios de Kantorovic, como estimador natural, y se prueba que su error absoluto medio es un infinitésimo de orden n-1/2. Mediante simulación se pone de manifiesto que dicho estimador conduce a una reducción sustancial del error absoluto medio frente a la función cuantil muestral y, por otra parte, se compara con el estimador basado en polinomios de Bernstein.

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.

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.

Evaluating improvements of records

Tomasz Rychlik (1997)

Applicationes Mathematicae

We evaluate the extreme differences between the consecutive expected record values appearing in an arbitrary i.i.d. sample in the standard deviation units. We also discuss the relevant estimates for parent distributions coming from restricted families and other scale units.

Evaluations of expected generalized order statistics in various scale units

Erhard Cramer, Udo Kamps, Tomasz Rychlik (2002)

Applicationes Mathematicae

We present sharp upper bounds for the deviations of expected generalized order statistics from the population mean in various scale units generated by central absolute moments. No restrictions are imposed on the parameters of the generalized order statistics model. The results are derived by combining the unimodality property of the uniform generalized order statistics with the Moriguti and Hölder inequalities. They generalize evaluations for specific models of ordered observations.

Exact laws for sums of ratios of order statistics from the Pareto distribution

André Adler (2006)

Open Mathematics

Consider independent and identically distributed random variables {X nk, 1 ≤ k ≤ m, n ≤ 1} from the Pareto distribution. We select two order statistics from each row, X n(i) ≤ X n(j), for 1 ≤ i < j ≤ = m. Then we test to see whether or not Laws of Large Numbers with nonzero limits exist for weighted sums of the random variables R ij = X n(j)/X n(i).

Extreme order statistics in an equally correlated Gaussian array

Mateusz Wiśniewski (1994)

Applicationes Mathematicae

This paper contains the results concerning the weak convergence of d-dimensional extreme order statistics in a Gaussian, equally correlated array. Three types of limit distributions are found and sufficient conditions for the existence of these distributions are given.

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