Evaluation of human bioclimates using quantile regression: A China case study

Pin T. Ng; Yuk Yee Yan

doi:10.2747/0272-3646.29.5.387

Evaluation of human bioclimates using quantile regression: A China case study

Pin T. Ng, Yuk Yee Yan

Business, The W.A. Franke College of (CoB)

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

1 Scopus citations

Abstract

A nonparametric version of quantile regressions was applied to investigate the human bioclimates in China through the use of the CLO (clothing requirements) index. Quantile regression surface maps produced from the whole spectrum of the CLO distribution provided better insights into the spatial characteristics of clothing requirements and information on the spread and variations of the distribution. Univariate quantile regression curves were constructed to display the temporal variability of CLO at different locations. Hence, this analysis produced a more complete picture of the human bioclimates of the various regions in China.

Original language	English (US)
Pages (from-to)	387-403
Number of pages	17
Journal	Physical Geography
Volume	29
Issue number	5
DOIs	https://doi.org/10.2747/0272-3646.29.5.387
State	Published - 2008

ASJC Scopus subject areas

General Environmental Science
Atmospheric Science
Earth and Planetary Sciences (miscellaneous)
General Earth and Planetary Sciences

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AB - A nonparametric version of quantile regressions was applied to investigate the human bioclimates in China through the use of the CLO (clothing requirements) index. Quantile regression surface maps produced from the whole spectrum of the CLO distribution provided better insights into the spatial characteristics of clothing requirements and information on the spread and variations of the distribution. Univariate quantile regression curves were constructed to display the temporal variability of CLO at different locations. Hence, this analysis produced a more complete picture of the human bioclimates of the various regions in China.

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Evaluation of human bioclimates using quantile regression: A China case study

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