Statistical Tests of the Joint Distribution of Spectral Acceleration Values

By N. Jayaram, Jack W. Baker. 2008

235 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/beb7febb8e4da63fe328fcb08054c09462660aad

How researchers see this paper

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

In Civil and Structural Engineering

1. Brendon Bradley
2. W. Du
3. Gang Wang
4. P. Bazzurro
5. M. Kohrangi
6. Jack W. Baker
7. P. Heresi
8. Eduardo Miranda

In Statistics and Probability

1. Brendon Bradley
2. P. Heresi
3. Eduardo Miranda
4. Jack W. Baker
5. S. Kotha
6. P. Bazzurro
7. M. Kohrangi
8. W. Du

Citations in the corpus

• Spatial Cross‐Correlation Models for Vector Intensity Measures (PGA, I a , PGV, and SAs) Considering Regional Site Conditions (2013, background): “Jayaram, N., and J. W. Baker (2008).”
• A simple ground‐motion prediction model for cumulative absolute velocity and model validation (2013, background): “Therefore, it is not necessary to normalize the inter-event residuals because of the homoscedasticity (as is also assumed in [18]).”
• Ground-motion models for average spectral acceleration in a period range: direct and indirect methods (2017, methodology): “Given the joint lognormal distribution of spectral accelerations at different periods for any given scenario (Jayaram and Baker 2008), and the assumption of lognormality of AvgSA distribution, the mean and variance of lnAvgSA can be indirectly estimated according to Eqs.”
• Correlation model for spatially distributed ground‐motion intensities (2009, background): “Jayaram and Baker [8] showed that a vector of spatially distributed intra-event residuals e j =( 1 j , 2 j , .”
• Conditional Spectrum Computation Incorporating Multiple Causal Earthquakes and Ground-Motion Prediction Models (2013, unknown): “…from Equation 3, when combined with the conditional mean value µ lnSa ( T i ) | lnSa ( T ∗ ) from Equation 2, specifies a complete distribution of log-arithmic spectral acceleration values at all periods (where the distribution at a given period is Gaussian, as justified by Jayaram and Baker (2008)).”
• Uncertainty in intraevent spatial correlation of elastic pseudo-acceleration spectral ordinates (2018, methodology): “The conditional distributions of the residuals for the rest of the periods, Ti, can be computed using a correlation model ρ0(Ti,Tmax), such as those proposed by Inoue and Cornell (1990), Baker and Cornell (2006), Abrahamson and Silva (2007), Baker and Jayaram (2008), or Abrahamson et al.”
• Considering Spatial Correlation in Mixed-Effects Regression and the Impact on Ground-Motion Models (2010, methodology): “These assumptions have been verified using recorded ground motions by Jayaram and Baker (2008).”
• A generalized conditional intensity measure approach and holistic ground‐motion selection (2010, background): “Based on the examination of the effect of the ground-motion parameter ε on spectral shape, and its acknowledged correlation at multiple vibration periods, Baker and Cornell [6] proposed the concept of a CMS considering ε.”