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Estimating precipitation extremes using the log‐histospline
Author(s) -
Huang Whitney K.,
Nychka Douglas W.,
Zhang Hao
Publication year - 2019
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/env.2543
Subject(s) - generalized pareto distribution , smoothing , histogram , mathematics , range (aeronautics) , remainder , statistics , pareto principle , computer science , extreme value theory , materials science , arithmetic , composite material , artificial intelligence , image (mathematics)
Abstract One of the commonly used approaches in modeling extremes is the peaks‐over‐threshold (POT) method. The POT method models exceedances over a threshold that is sufficiently high so that the exceedance has approximately a generalized Pareto distribution. This method requires the selection of a threshold that might affect the estimates. Here, we propose an alternative method, the log‐histospline (LHSpline), to explore modeling the tail behavior and the remainder of the density in one step using the full range of the data. LHSpline applies a smoothing spline model to a finely binned histogram of the log‐transformed data to estimate its log density. By construction, a LHSpline estimation is constrained to have polynomial tail behavior, a feature commonly observed in daily rainfall observations. We illustrate the LHSpline method by analyzing precipitation data collected in Houston, Texas.