Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page
Demo Data from SIGIR 2026 / reviewers in the wild / expert

Soon-Sen Lau

dblp:30/3138 · DBLP profile ↗
← Back
2ranked-venue papers
1as first author
0since 2021 · last 2002
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1Theory of computation · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
1 paper
Information theory · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Information theory › signal processing
spectral estimation		 0.012002
Asymptotic statistical properties of AR Spectral estimators for processes with mixed spectra · IEEE Trans. Inf. Theory 2002
Information theory › signal processing
statistical signal processing		 0.012002
Asymptotic statistical properties of AR Spectral estimators for processes with mixed spectra · IEEE Trans. Inf. Theory 2002

Methods — techniques the papers use, named apart from their topics

asymptotic analysis · 0.0
YearPublicationVenuePosition
2002 Asymptotic statistical properties of AR Spectral estimators for processes with mixed spectra
abstract
The influence of a point spectrum on large sample statistics of the autoregressive (AR) spectral estimator is addressed. In particular, the asymptotic distributions of the AR coefficients, the innovations variance, and the spectral density estimator of a finite-order AR(p) model to a mixed spectrum process are presented. Various asymptotic results regarding AR modeling of a regular process with a continuous spectrum are arrived at as special cases of the results for the mixed spectrum setting. Finally, numerical simulations are performed to verify the analytical results.
Soon-Sen Lau, Peter J. Sherman, Langford B. White
 IEEE Trans. Inf. Theory1
1998 Asymptotic statistical properties of autoregressive model for mixed spectrum estimation
abstract
This work addresses the influence of the point spectrum on large sample statistics of the autoregressive spectral estimator. In particular, the asymptotic distributions of the AR coefficients, the innovations variance, and the spectral density estimator of a finite order AR(p) model for a mixed spectrum process are presented. Numerical simulations are performed to verify the analytical results.
Peter J. Sherman, Soon-Sen Lau
 ICASSP2