On model selection criteria in multimodel analysis

By Ming Ye, P. Meyer, Shlomo P. Neuman. 2007

269 citations recorded by the source corpus.

Subfields: Environmental Engineering, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/e2208cd7b2049088f74927c054c2e1a169af57cc

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Environmental Engineering

1. Jasper A. Vrugt
2. Ming Ye
3. M. Clark
4. Hoshin V. Gupta
5. W. Durner
6. S. Iden
7. E. Diamantopoulos
8. Gab Abramowitz

In Water Science and Technology

1. Jasper A. Vrugt
2. Ming Ye
3. M. Clark
4. Hoshin V. Gupta
5. Gab Abramowitz
6. W. Durner
7. S. Iden
8. E. Diamantopoulos

Citations in the corpus

• A Bayesian approach for inverse modeling, data assimilation, and conditional simulation of spatial random fields (2010, methodology): “This is the approach pursued by Hoeting et al. [1999], Neuman [2003], and Ye et al. [2008] , which we apply here for the MAD algorithm.”
• Quantification of uncertainty in pedotransfer function‐based parameter estimation for unsaturated flow modeling (2009, methodology): “Pachepsky et al. [2006] suggested that the recently developed method of Bayesian model averaging [Neuman, 2003; Ye et al., 2004, 2005a, 2008 ] is a promising tool for addressing the PTF model uncertainty.”
• Towards a comprehensive assessment of model structural adequacy (2012, methodology): “…Bayesian Model Averaging (MLBMA) approach assigns relative weights to eachmodel based on their ability to explain the observed data [Neuman, 2003; Ye et al., 2004, 2008a]; the relative weights can also be obtained using other methods [Poeter and Anderson, 2005; Poeter and Hill, 2007; Tsai and…”
• Inverse modeling of dynamic nonequilibrium in water flow with an effective approach (2012, methodology): “The Bayesian information criterion (BIC) was computed as [Ye et al., 2008] BIC ¼ 2 ln fpostðp̂jyÞ þ n lnðmÞ (23) [24] The most frequently applied Bayesian measure of complexity and fit is the deviance information criterion (DIC) defined as [Spiegelhalter et al., 2002] DIC ¼ DðpÞ þ 2pD ¼ DðpÞ þ pD…”
• A comparison of seven methods for the inverse modelling of groundwater flow. Application to the characterisation of well catchments (2009, background): “A second reason is that conceptual uncertainties are difficult to be formalized in a rigorous mathematical framework [109,138].”