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Estimating Quantile Sensitivities
Operations Research, 2009Quantiles of a random performance serve as important alternatives to the usual expected value. They are used in the financial industry as measures of risk and in the service industry as measures of service quality. To manage the quantile of a performance, we need to know how changes in the input parameters affect the output quantiles, which are called
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Estimating densities, quantiles, quantile densities and density quantiles
Annals of the Institute of Statistical Mathematics, 1992zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Estimating Equivalence with Quantile Regression
Ecological Applications, 2010Equivalence testing and corresponding confidence interval estimates are used to provide more enlightened statistical statements about parameter estimates by relating them to intervals of effect sizes deemed to be of scientific or practical importance rather than just to an effect size of zero.
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Quantile Estimation from Repeated Measurements
Journal of the American Statistical Association, 1996Abstract Quantile estimators for a nonparametric components of variance situation are proposed consistency and asymptotic normality are proved. Situations with different numbers of measurements for different subjects are considered. Measurements on separate subjects are assumed to be independent, whereas measurements on the same subject have a fixed ...
J. Olsson, H. Rootzen
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Quantile Sensitivity Estimation
2009Quantiles are important performance characteristics that have been adopted in many areas for measuring the quality of service. Recently, sensitivity analysis of quantiles has attracted quite some attention. Sensitivity analysis of quantiles is particularly challenging as quantiles cannot be expressed as the expected value of some sample performance ...
Heidergott, B.F., Volk-Makarewicz, W.M.
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Quantile Estimation in Dependent Sequences
Operations Research, 1984Standard nonparametric estimators of quantiles based on order statistics can be used not only when the data are i.i.d., but also when the data are drawn from a stationary, Ï•-mixing process of continuous random variables. However, when the random variables are highly positively correlated, the sample sizes needed for estimating extreme quantiles become
Heidelberger, P., Lewis, P. A. W.
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Bayesian Quantile Estimation in Deconvolution
2022Estimating quantiles of a population is a fundamental problem in nonparametric statistics, with high practical relevance. This note deals, from a Bayesian point of view, with quantile estimation in deconvolution problems with known error distribution. We pursue the analysis for error distributions whose characteristic functions decay polynomially fast,
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Optimise importance sampling quantile estimation
Biometrika, 1996This paper considers the use of an auxiliary variable X* to estimate quantiles of a test statistic X; X* may be an asymptotic expansion of X, or a simplified version which ignores some of the convariance structure. The proposed estimator involves three stages. First a large sample is drawn, and X* is evaluated. Then a first subsample is drawn, X and X*
Goffinet, Bruno, Wallach, Daniel
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Communications in Statistics - Theory and Methods, 1983
As an alternative to the conventional sample quantile, Kaigh and Lachenbruch (1982) propose a certain U-statistic as a non-parametric estimator of a continuous population quantile. Other related quantile estimators are introduced and studied here. The jackknife procedure is applied to obtain sample variance estimates for the construction of symmetric ...
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As an alternative to the conventional sample quantile, Kaigh and Lachenbruch (1982) propose a certain U-statistic as a non-parametric estimator of a continuous population quantile. Other related quantile estimators are introduced and studied here. The jackknife procedure is applied to obtain sample variance estimates for the construction of symmetric ...
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Quantile Regression Neural Network for Quantile Claim Amount Estimation
2021Quantile Regression to estimate the conditional quantile of the claim amount for car insurance policies has already been by Heras et al. (2018) and others. In this paper, we explore two alternative approaches, the first involves Quantile Regression Neural Networks (QRNN), while the second is an extension of the Combined Actuarial Neural Network (CANN ...
Laporta, Alessandro G. +2 more
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