Smoothing Spline ANOVA for Time-Dependent Spectral Analysis

By Wensheng Guo, Ming-Shiang Dai, Hernando C. Ombao, R. von Sachs. 2003

50 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/b5645a7638acafeaaffcd45504d032d4449ef2cc

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In Computer Vision and Pattern Recognition

1. David S. Stoffer

Citations in the corpus

• Nonparametric quasi-maximum likelihood estimation for Gaussian locally stationary processes (2006, unknown): “er functions like α(·) or σ(·) then follow from the former results for spectral densities. The time-varying AR(1)-process from above will serve as a simple example for our general results. Guo et al. [16] consider an approach for nonparametric estimation of the time-varying spectral density using bo”
• Local Spectral Analysis via a Bayesian Mixture of Smoothing Splines (2009, background): “KEY WORDS: Evolutionary spectra; Locally stationary time series; Mixture of splines; Reversible jump Markov chain Monte Carlo.”
• Functional Regression (2014, unknown): “Their approach can be extended to locally stationary time series by partitioning time and using the tensor product approach of Guo (2003).”