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Modeling restricted bivariate censored lowflow data
Author(s) -
Lu JyeChyi,
Liu Shiping,
Yin Ming,
HughesOliver Jacqueline M.
Publication year - 1999
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/(sici)1099-095x(199903/04)10:2<125::aid-env340>3.0.co;2-y
Subject(s) - quantile , bivariate analysis , univariate , statistics , econometrics , mathematics , streamflow , quantile regression , multivariate statistics , index (typography) , bivariate data , drainage basin , computer science , geography , cartography , world wide web
Environmental studies often result in censored data. In this article, the lowflow quantiles Q* 7,2 and Q* 7,10 below a limit are treated as censored data. These streamflow quantiles are important for water resources planning and management. Our partial all‐subsets censored regression procedure identifies a few important explanatory variables, such as drainage area, basin slope, soil‐infiltration index, rainfall index, and some combinations of them. The proposed maximum likelihood estimation method incorporates the restriction Q* 7,2 ≥Q* 7,10 and the bivariate probability distribution of the quantiles to improve model quality. Analyses of the lowflow quantiles obtained from streams in West‐Central Florida show that our procedure is more appropriate than the commonly used univariate main‐effects models in predicting quantiles. Copyright © 1999 John Wiley & Sons, Ltd.