Probabilistic evaluation of quantile estimators

Matti Pajari; Maria Tikanmäki; Lasse Makkonen

doi:10.1080/03610926.2019.1696975

Probabilistic evaluation of quantile estimators

Matti Pajari
, Maria Tikanmäki
, Lasse Makkonen^*

^*Corresponding author for this work

Berakon

Research output: Contribution to journal › Article › Scientific › peer-review

5 Citations (Scopus)

Abstract

The foundations of the criteria to assess the goodness of quantile estimators for continuous random variables are reviewed and the probabilistic justification for a novel bin-criterion is presented. It is shown that the bin-criterion is a more appropriate measure of goodness of a quantile estimator than those based on minimizing the bias of the quantiles or the parameters of the distribution.

Original language	English
Pages (from-to)	3319-3337
Journal	Communications in Statistics: Theory and Methods
Volume	50
Issue number	14
Early online date	10 Dec 2019
DOIs	https://doi.org/10.1080/03610926.2019.1696975
Publication status	Published - 2021
MoE publication type	A1 Journal article-refereed

Funding

This research was supported by the Academy of Finland, grants no. 140884 and 268925.

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10.1080/03610926.2019.1696975

Cite this

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abstract = "The foundations of the criteria to assess the goodness of quantile estimators for continuous random variables are reviewed and the probabilistic justification for a novel bin-criterion is presented. It is shown that the bin-criterion is a more appropriate measure of goodness of a quantile estimator than those based on minimizing the bias of the quantiles or the parameters of the distribution.",

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AU - Pajari, Matti

AU - Tikanmäki, Maria

AU - Makkonen, Lasse

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AB - The foundations of the criteria to assess the goodness of quantile estimators for continuous random variables are reviewed and the probabilistic justification for a novel bin-criterion is presented. It is shown that the bin-criterion is a more appropriate measure of goodness of a quantile estimator than those based on minimizing the bias of the quantiles or the parameters of the distribution.

UR - https://www.scopus.com/pages/publications/85076398170

U2 - 10.1080/03610926.2019.1696975

DO - 10.1080/03610926.2019.1696975

M3 - Article

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VL - 50

SP - 3319

EP - 3337

JO - Communications in Statistics: Theory and Methods

JF - Communications in Statistics: Theory and Methods

IS - 14

ER -

Probabilistic evaluation of quantile estimators

Abstract

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