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Showing 1 to 11 of 11 for “"Quantile estimation"”.
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Smooth regression quantile estimation
… focused on the local linear kernel regression quantile estimation. Different estimators within this class have been proposed, developed asymptotically and applied to real applications. I include algorithmdesign and selection of smoothing parameters. Chapter 2 studies two estimators, first a …
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Conditional Quantile Estimation With Ordinal Data
… regression method for estimating the conditional quantiles of the ordinal response variable. By assuming a continuous latent variable underlying the observed ordinal response variable and utilizing the equivalence property of quantile regression we obtain the estimates of the conditional quantile …
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Effective Estimation of Marginal Quantiles in Steady-State Simulations
… central tendency, a (marginal) steady-state quantile characterizes the long-run risk associated with the individual realizations. The estimation of a steady-state quantile is typically a substantially harder problem than the estimation of the mean: while both problems are subject to effects …
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Sequential nonparametric estimation via Hermite series estimators
… novel approaches to sequential (online) estimation in both stationary and non-stationary settings based on Hermite series density estimators. In the univariate context we apply Hermite series based distribution function estimators to sequential cumulative distribution function estimation. …
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One and Two-Step Estimation of Time Variant Parameters and Nonparametric Quantiles
… smoothing methods of time variant nonparametric quantiles and time variant parameters from probability models. First, we investigate and develop nonparametric techniques for measuring extreme quantiles. The method involves aggregating data by an explanatory variable such as time and smoothing the …
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Approximation of Quantiles of Rank Test Statistics Using Almost Sure Limit Theorems
… One possible solution is the logarithmic quantile estimation (LQE) method introduced by Thangavelu (2005) for rank tests and Fridline (2010) for the correlation coefficient. LQE is derived from an almost sure version of the central limit theorem using the results of Berkes and Csaki …
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Variance reduction techniques for estimating quantiles and value-at-risk
Quantiles, as a performance measure, arise in many practical contexts. In finance, quantiles are called values-at-risk (VARs), and they are widely used in the financial industry to measure portfolio risk. When the cumulative distribution function is unknown, the quantile can not be computed exactly …
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Συμβολή στη στατιστική συμπερασματολογία για τις κατανομές γάμα και αντίστροφη κανονική με χρήση της εμπειρικής ροπογεννήτριας συνάρτησης
Το αντικείμενο της παρούσας διατριβής είναι η διερεύνηση μεθόδων στατιστικής συμπερασματολογίας για την προσαρμογή και έλεγχο της κατανομής γάμα και της αντίστροφης κανονικής (inverse Gaussian) κατανομής σε δεδομένα με θετική λοξότητα. Τα πρότυπα αυτά χρησιμοποιούνται ευρέως στην ανάλυση …
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Étude des propriétés des estimateurs pour le modèle de régression quantile
"Dès son introduction, la régression quantile a connu plusieurs développements dans différents domaines de la statistique appliquée. À la différence de l'approche classique, la régression quantile s'intéresse à l'ensemble de la distribution et non seulement à la partie centrale. En …