H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 46 Citations 43,864 144 World Ranking 3382 National Ranking 1745

Research.com Recognitions

Awards & Achievements

1989 - IEEE Fellow For contributions to the theory and application of parametric spectral estimation and detection.

Overview

What is he best known for?

The fields of study he is best known for:

  • Statistics
  • Artificial intelligence
  • Algorithm

His primary areas of study are Algorithm, Gaussian noise, Estimator, Statistics and Estimation theory. His biological study spans a wide range of topics, including Bias of an estimator, Speech recognition, Chirp and Spectral density estimation, Signal. His studies deal with areas such as Detection theory, Additive white Gaussian noise, White noise and Signal processing as well as Gaussian noise.

Steven Kay has researched Estimator in several fields, including Signal-to-noise ratio, Mean squared error, Applied mathematics and Autoregressive model. His Estimation theory study combines topics from a wide range of disciplines, such as Artificial intelligence and Pattern recognition. His Artificial intelligence research integrates issues from Cramér–Rao bound and Statistical signal processing.

His most cited work include:

  • Fundamentals of statistical signal processing: estimation theory (11252 citations)
  • Fundamentals Of Statistical Signal Processing (3605 citations)
  • Spectrum analysis—A modern perspective (2772 citations)

What are the main themes of his work throughout his whole career to date?

Steven Kay spends much of his time researching Algorithm, Artificial intelligence, Statistics, Autoregressive model and Applied mathematics. His work in Estimation theory and Gaussian noise are all subfields of Algorithm research. His research integrates issues of Sufficient statistic and Mathematical optimization in his study of Estimation theory.

The Artificial intelligence study combines topics in areas such as Exponential family, Machine learning, Signal, Signal processing and Pattern recognition. His studies in Autoregressive model integrate themes in fields like Spectral density estimation, Parametric statistics, Time series and Autocorrelation. His Applied mathematics study incorporates themes from Statistical hypothesis testing, Probability density function and Random variable.

He most often published in these fields:

  • Algorithm (39.91%)
  • Artificial intelligence (22.07%)
  • Statistics (21.60%)

What were the highlights of his more recent work (between 2011-2021)?

  • Artificial intelligence (22.07%)
  • Detector (20.19%)
  • Algorithm (39.91%)

In recent papers he was focusing on the following fields of study:

His scientific interests lie mostly in Artificial intelligence, Detector, Algorithm, Pattern recognition and Applied mathematics. He combines subjects such as Detection theory, Machine learning, Probability theory and Estimation theory with his study of Artificial intelligence. His work carried out in the field of Estimation theory brings together such families of science as Image resolution and Topology.

The various areas that he examines in his Algorithm study include Mathematical optimization, Autoregressive model and Autocorrelation. His Pattern recognition research includes themes of Exponential family and Parametric statistics. His work is dedicated to discovering how Cramér–Rao bound, Fisher information are connected with Upper and lower bounds and other disciplines.

Between 2011 and 2021, his most popular works were:

  • Toward Optimal Feature Selection in Naive Bayes for Text Categorization (118 citations)
  • A Bayesian Classification Approach Using Class-Specific Features for Text Categorization (90 citations)
  • TDOA based direct positioning maximum likelihood estimator and the cramer-rao bound (64 citations)

In his most recent research, the most cited papers focused on:

  • Statistics
  • Artificial intelligence
  • Random variable

Artificial intelligence, Pattern recognition, Algorithm, Detector and Feature selection are his primary areas of study. His Artificial intelligence study combines topics in areas such as Estimation theory, Detection theory, Probability density function and Density estimation. His study in the field of Naive Bayes classifier is also linked to topics like Multivariate kernel density estimation.

His work deals with themes such as Speech recognition and Electronic engineering, which intersect with Algorithm. His Detector research incorporates themes from Signal-to-noise ratio, Waveform, Likelihood-ratio test, Autoregressive model and Colors of noise. His Autoregressive model research integrates issues from Constant false alarm rate, Laser and Noise.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Fundamentals of statistical signal processing: estimation theory

Steven M. Kay.
Technometrics (1993)

20753 Citations

Fundamentals Of Statistical Signal Processing

Steven M. Kay.
(1993)

6536 Citations

Modern Spectral Estimation: Theory and Application

Steven M. Kay.
(1988)

5303 Citations

Spectrum analysis—A modern perspective

S.M. Kay;S.L. Marple.
Proceedings of the IEEE (1981)

4185 Citations

A fast and accurate single frequency estimator

S. Kay.
IEEE Transactions on Acoustics, Speech, and Signal Processing (1989)

1052 Citations

Parameter estimation of chirp signals

P.M. Djuric;S.M. Kay.
IEEE Transactions on Acoustics, Speech, and Signal Processing (1990)

527 Citations

Fractional Brownian Motion: A Maximum Likelihood Estimator and Its Application to Image Texture

Torbjorn Lundahl;William J. Ohley;Steven M. Kay;Robert Siffert.
IEEE Transactions on Medical Imaging (1986)

495 Citations

Digital signal processing for sonar

W.C. Knight;R.G. Pridham;S.M. Kay.
Proceedings of the IEEE (1981)

396 Citations

Theory of the Stochastic Resonance Effect in Signal Detection—Part II: Variable Detectors

Hao Chen;P.K. Varshney;S.M. Kay;J.H. Michels.
IEEE Transactions on Signal Processing (2007)

303 Citations

Can detectability be improved by adding noise

S. Kay.
IEEE Signal Processing Letters (2000)

265 Citations

If you think any of the details on this page are incorrect, let us know.

Contact us

Best Scientists Citing Steven Kay

Petre Stoica

Petre Stoica

Uppsala University

Publications: 118

Arye Nehorai

Arye Nehorai

Washington University in St. Louis

Publications: 105

Georgios B. Giannakis

Georgios B. Giannakis

University of Minnesota

Publications: 94

Hing Cheung So

Hing Cheung So

City University of Hong Kong

Publications: 87

Erik G. Larsson

Erik G. Larsson

Linköping University

Publications: 84

Pramod K. Varshney

Pramod K. Varshney

Syracuse University

Publications: 83

Hongbin Li

Hongbin Li

Stevens Institute of Technology

Publications: 80

Peter Händel

Peter Händel

Royal Institute of Technology

Publications: 80

Emil Bjornson

Emil Bjornson

Royal Institute of Technology

Publications: 78

Abdelhak M. Zoubir

Abdelhak M. Zoubir

TU Darmstadt

Publications: 66

Geert Leus

Geert Leus

Delft University of Technology

Publications: 62

Sinan Gezici

Sinan Gezici

Bilkent University

Publications: 61

Fulvio Gini

Fulvio Gini

University of Pisa

Publications: 59

Franz Hlawatsch

Franz Hlawatsch

TU Wien

Publications: 54

Yonina C. Eldar

Yonina C. Eldar

Weizmann Institute of Science

Publications: 54

Bjorn Ottersten

Bjorn Ottersten

University of Luxembourg

Publications: 53

Something went wrong. Please try again later.