Electronic Journal of Statistics

Bayesian estimation of Huff curves

Nilabja Guha, Ishani Roy, and Anindya Roy

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We propose a nonparametric Bayesian method for estimating regression functions that arise as cumulative distribution functions (cdfs) of a stochastically ordered family of distribution supported on [0,1]. The motivating example is estimation of Huff curves which are depth duration curves for heavy storm rainfall. The Bayesian methodology is compared with the linear programming based estimation method that is currently used by the National Oceanic and Atmospheric Administration (NOAA) for producing the Huff curves. The methodology is illustrated with the rainfall data from the rain gauge stations in California, US. Some limited simulation results are provided to illustrate the finite sample performance of the proposed estimator. We also establish consistency of the proposed method.

Article information

Electron. J. Statist., Volume 7 (2013), 2794-2821.

First available in Project Euclid: 2 December 2013

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62P12: Applications to environmental and related topics 62G08: Nonparametric regression
Secondary: 90C05: Linear programming

Storm frequency hyetograph linear programming stochastic ordering Bayesian estimation Dirichlet process Bernstein polynomial


Guha, Nilabja; Roy, Ishani; Roy, Anindya. Bayesian estimation of Huff curves. Electron. J. Statist. 7 (2013), 2794--2821. doi:10.1214/13-EJS862. https://projecteuclid.org/euclid.ejs/1385995291

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