Bayesian generalized least squares regression with application to log Pearson type 3 regional skew estimation

By Dirceu Silveira Reis, Jery R. Stedinger, Eduardo Sávio Passos Rodrigues Martins. 2005

116 citations recorded by the source corpus.

Subfields: Environmental Engineering, Ocean Engineering, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/8348cb42c4fcfdec96a417e8b78ba5ea7ffa8869

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In Environmental Engineering

In Ocean Engineering

In Water Science and Technology

Citations in the corpus

Regional flood frequency analysis using Bayesian generalized least squares: a comparison between quantile and parameter regression techniques (2012, methodology): “These results indicate that the developed prediction equations satisfy the homogeneity and normality of the residual assumptions Copyright © 2011 John Wiley & Sons, Ltd. Hydrol.”
A parametric Bayesian combination of local and regional information in flood frequency analysis (2006, methodology): “A mixed approach can also be Bayesian: for instance, a Bayesian approach was used by Reis et al. [2003, 2005] to infer the skew coefficient of the LP3 distribution while using local data to compute the two other parameters.”
An exploratory analysis of error components in hydrological regression modeling (2009, methodology): “Unfortunately, the statistical properties of the regression model errors cannot be estimated by a simple extension of least squares methods, but have to be obtained via a recursive procedure, such as outlined here, or via a maximum likelihood or Bayesian approach [Reis et al., 2005].”
The use of GLS regression in regional hydrologic analyses (2007, methodology): “Reis et al. (2005) and Gruber et al. (2007) suggest a Bayesian analysis of the GLS model for hydrologic analyses.”
A Bayesian hierarchical approach to regional frequency analysis (2011, background): “Regression Errors [43] Regression errors ek ¼ ð"k ½s Þs¼1:M are commonly assumed to be spatially independent [e.g., Stedinger and Tasker, 1985, 1986; Reis et al., 2005].”
Groundwater level prediction in arid areas using wavelet analysis and Gaussian process regression (2021, background): “It deals with certain aspects of regression and prediction problems (Reis et al., 2005; Stedinger & Tasker, 1985; Vogel et al., 1999).”