Using recursive algorithms for the efficient identification of smoothing spline ANOVA models

By Marco Ratto, Andrea Pagano. 2010

77 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/dfd638e694dc51e89a4542bc77cc7394747d332b

How researchers see this paper

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

In Control and Systems Engineering

1. Michaela Saisana
2. Andrea Saltelli
3. Paolo Paruolo

In Statistics and Probability

1. Michaela Saisana
2. Paolo Paruolo
3. Saman Razavi
4. Bryan Tolson
5. Donald H. Burn
6. Andrea Saltelli
7. Stefano Tarantola
8. Thorsten Wagener

Citations in the corpus

• Ratings and rankings: voodoo or science? (2011, methodology): “Many nonparametric estimators can be used to estimate fi(xi), such as State Dependent Regression: Ratto et al. (2007); Ratto and Pagano (2010).”
• Review of surrogate modeling in water resources (2012, background): “…Sobol, 2003], treed Gaussian processes [Gramacy and Lee, 2008], Gaussian emulator machines [Kennedy and O’Hagan, 2001; O’Hagan, 2006], smoothing splines ANOVA models [Gu, 2002; Ratto and Pagano, 2010; Storlie et al., 2011; Wahba, 1990], and proper orthogonal decomposition [Audouze et al., 2009].”
• Numerical assessment of metamodelling strategies in computationally intensive optimization (2012, background): “However, Ratto and Pagano (2010) showed that the most advanced smoothing (non-interpolating) methods compare favourably with kriging which is an interpolant.”