Two-Stage support estimation

S. Mueller

doi:10.1111/j.1467-842X.2005.00409.x

Two-Stage support estimation

S. Mueller

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

1 Citation (Scopus)

Abstract

This paper presents a two-stage procedure for estimating the conditional support curve of a random variable X, given the information of a random vector X. Quantile estimation is followed by an extremal analysis on the residuals for problems which can be written as regression models. The technique is applied to data from the National Bureau of Economic Research and US Census Bureau's Center for Economic Studies which contain all four-digit manufacturing industries. Simulation results show that in linear regression models the proposed estimation procedure is more efficient than the extreme linear regression quantile.

Original language	English
Pages (from-to)	463-472
Journal	Australian & New Zealand Journal of Statistics
Volume	47
Issue number	4
DOIs	https://doi.org/10.1111/j.1467-842X.2005.00409.x
Publication status	Published - 2005

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10.1111/j.1467-842X.2005.00409.x

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title = "Two-Stage support estimation",

abstract = "This paper presents a two-stage procedure for estimating the conditional support curve of a random variable X, given the information of a random vector X. Quantile estimation is followed by an extremal analysis on the residuals for problems which can be written as regression models. The technique is applied to data from the National Bureau of Economic Research and US Census Bureau's Center for Economic Studies which contain all four-digit manufacturing industries. Simulation results show that in linear regression models the proposed estimation procedure is more efficient than the extreme linear regression quantile.",

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AB - This paper presents a two-stage procedure for estimating the conditional support curve of a random variable X, given the information of a random vector X. Quantile estimation is followed by an extremal analysis on the residuals for problems which can be written as regression models. The technique is applied to data from the National Bureau of Economic Research and US Census Bureau's Center for Economic Studies which contain all four-digit manufacturing industries. Simulation results show that in linear regression models the proposed estimation procedure is more efficient than the extreme linear regression quantile.

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DO - 10.1111/j.1467-842X.2005.00409.x

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EP - 472

JO - Australian & New Zealand Journal of Statistics

JF - Australian & New Zealand Journal of Statistics

IS - 4

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Two-Stage support estimation

Abstract

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