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VLDB 2026 Research / reviewers in the wild / expert

Gene Barry Goldstein

dblp:134/4263 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 1970
—ORCID · none

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

Theory 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
Mathematical optimization · 67% Information theory · 33%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Mathematical optimization
bootstrap		 0.011970
Bootstrapping the generalized least-squares estimator in colored Gaussian noise with unknown covariance parameters · IEEE Trans. Inf. Theory 1970
Information theory › communication channels › channel models › gaussian noise
colored gaussian noise		 0.011970
Bootstrapping the generalized least-squares estimator in colored Gaussian noise with unknown covariance parameters · IEEE Trans. Inf. Theory 1970
Mathematical optimization
statistical estimation		 0.011970
Bootstrapping the generalized least-squares estimator in colored Gaussian noise with unknown covariance parameters · IEEE Trans. Inf. Theory 1970

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

least-squares estimation · 0.0bootstrapping · 0.0
YearPublicationVenuePosition
1970 Bootstrapping the generalized least-squares estimator in colored Gaussian noise with unknown covariance parameters
abstract
It is demonstrated that in problems involving the estimation of linear regression parameters in colored Gaussian noise, the simple least-squares estimator can be significantly suboptimal. When the noise covariance function can be described as a known function of a finite number of unknown nonrandom parameters, it is possible to take advantage of this information to improve upon the least-squares estimator by an appropriate bootstrapping technique. Two examples are given, and comments that may lead to other examples are presented.
Gene Barry Goldstein, Peter Swerling
 IEEE Trans. Inf. Theory1