Asymptotic normality of convergent estimates of conditional quantiles

Alain Berlinet; Ali Gannoun; Eric Matzner-Løber

doi:10.1080/02331880108802728

Asymptotic normality of convergent estimates of conditional quantiles

Alain Berlinet
, Ali Gannoun
, Eric Matzner-Løber

University of Montpellier (UMR MiVEGEC)

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

Abstract

We state sufficient conditions for asymptotic normality of convergent estimates of conditional quantiles, irrespective of data dependence and consider the particular case of α-mixing stationary processes under optimal condition of convergence. We apply this result to confidence intervals building for time series predictors based on nonparametric estimates of the conditional median.

Original language	English
Pages (from-to)	139-169
Number of pages	31
Journal	Statistics
Volume	35
Issue number	2
DOIs	https://doi.org/10.1080/02331880108802728
Publication status	Published - 1 Jan 2001
Externally published	Yes

Keywords

Asymptotic normality
Conditional quantiles
Forecasting
Time series
α-mixing stationary processes

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10.1080/02331880108802728

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title = "Asymptotic normality of convergent estimates of conditional quantiles",

abstract = "We state sufficient conditions for asymptotic normality of convergent estimates of conditional quantiles, irrespective of data dependence and consider the particular case of α-mixing stationary processes under optimal condition of convergence. We apply this result to confidence intervals building for time series predictors based on nonparametric estimates of the conditional median.",

keywords = "Asymptotic normality, Conditional quantiles, Forecasting, Time series, α-mixing stationary processes",

author = "Alain Berlinet and Ali Gannoun and Eric Matzner-L{\o}ber",

year = "2001",

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doi = "10.1080/02331880108802728",

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AU - Berlinet, Alain

AU - Gannoun, Ali

AU - Matzner-Løber, Eric

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N2 - We state sufficient conditions for asymptotic normality of convergent estimates of conditional quantiles, irrespective of data dependence and consider the particular case of α-mixing stationary processes under optimal condition of convergence. We apply this result to confidence intervals building for time series predictors based on nonparametric estimates of the conditional median.

AB - We state sufficient conditions for asymptotic normality of convergent estimates of conditional quantiles, irrespective of data dependence and consider the particular case of α-mixing stationary processes under optimal condition of convergence. We apply this result to confidence intervals building for time series predictors based on nonparametric estimates of the conditional median.

KW - Asymptotic normality

KW - Conditional quantiles

KW - Forecasting

KW - Time series

KW - α-mixing stationary processes

UR - https://www.scopus.com/pages/publications/0000859323

U2 - 10.1080/02331880108802728

DO - 10.1080/02331880108802728

M3 - Article

AN - SCOPUS:0000859323

SN - 0233-1888

VL - 35

SP - 139

EP - 169

JO - Statistics

JF - Statistics

IS - 2

ER -

Asymptotic normality of convergent estimates of conditional quantiles

Abstract

Keywords

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