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Mathematics

D-Index
33
Citations
5496
World Ranking
3023
National Ranking
1218

Overview

David E. Tyler is affiliated with Rutgers, The State University of New Jersey in the United States. Their research spans multiple fields, primarily focusing on Mathematics and Computer Science with an emphasis on Statistics and Probability, Artificial Intelligence, Computational Mechanics, Economics and Econometrics, and Signal Processing.

The main topics covered in their work include Advanced Statistical Methods and Models, Sparse and Compressive Sensing Techniques, Bayesian Methods and Mixture Models, Statistical Methods and Bayesian Inference, Spatial and Panel Data Analysis, Blind Source Separation Techniques, and Spectroscopy and Chemometric Analyses.

Recent publications by David E. Tyler include:

  • Asymptotic and bootstrap tests for subspace dimension, 2021, Journal of Multivariate Analysis
  • Shrinking the Covariance Matrix Using Convex Penalties on the Matrix-Log Transformation, 2020, Journal of Computational and Graphical Statistics
  • On the variability of the sample covariance matrix under complex elliptical distributions, 2021, arXiv (Cornell University)
  • On the Variability of the Sample Covariance Matrix Under Complex Elliptical Distributions, 2021, IEEE Signal Processing Letters
  • Linear Pooling of Sample Covariance Matrices, 2021, IEEE Transactions on Signal Processing

David E. Tyler collaborates frequently with several co-authors, including Mengxi Yi, Elias Raninen, Esa Ollila, Klaus Nordhausen, and Hannu Oja.

Their work is published in venues such as:

  • arXiv (Cornell University)
  • Journal of Multivariate Analysis
  • Journal of Computational and Graphical Statistics
  • IEEE Signal Processing Letters
  • IEEE Transactions on Signal Processing

Best Publications

  • A Distribution-Free $M$-Estimator of Multivariate Scatter

    David E. Tyler

  • Complex Elliptically Symmetric Distributions: Survey, New Results and Applications

    E. Ollila;D. E. Tyler;V. Koivunen;H. V. Poor

  • Redescending $M$-Estimates of Multivariate Location and Scatter

    John T. Kent;David E. Tyler

  • Statistical analysis for the angular central Gaussian distribution on the sphere

    David E. Tyler

  • Robustness and efficiency properties of scatter matrices

    David E. Tyler

  • Asymptotic Inference for Eigenvectors

    David E. Tyler

  • Invariant co-ordinate selection

    David E. Tyler;Frank Critchley;Lutz Dümbgen;Hannu Oja

  • On the uniqueness of S-functionals and M-functionals under nonelliptical distributions

    Kay S. Tatsuoka;David E. Tyler

  • Maximum likelihood estimation for the wrapped Cauchy distribution

    John T. Kent;David E. Tyler

  • Compound-Gaussian Clutter Modeling With an Inverse Gaussian Texture Distribution

    E. Ollila;D. E. Tyler;V. Koivunen;H. V. Poor

  • ON WIELANDT'S INEQUALITY AND ITS APPLICATION TO THE ASYMPTOTIC DISTRIBUTION OF THE EIGENVALUES OF A RANDOM SYMMETRIC MATRIX

    Morris L. Eaton;David E. Tyler

  • Finite Sample Breakdown Points of Projection Based Multivariate Location and Scatter Statistics

    David E. Tyler

  • Radial estimates and the test for sphericity

    David E. Tyler

  • Constrained M-estimation for multivariate location and scatter

    John T. Kent;David E. Tyler

  • Regularized M-estimators of scatter matrix

    Esa Ollila;David E. Tyler

  • A curious likelihood identity for the multivariate t-distribution

    John T. Kent;David E. Tyler;Yahuda. Vard

  • Spectral analysis for categorical time series: Scaling and the spectral envelope

    David S. Stoffer;David E. Tyler;Andrew J. Mcdougall

  • Robust functional principal components: A projection-pursuit approach

    Juan Lucas Bali;Graciela Lina Boente Boente;David E. Tyler;Jane Ling Wang

  • On the Breakdown Properties of Some Multivariate M‐Functionals*

    Lutz Dümbgen;David E. Tyler

  • Robust regression for data with multiple structures

    Haifeng Chen;P. Meer;D.E. Tyler

  • Robust Statistics: Theory and Methods

    David E Tyler

  • Robust functional principal components: A projection-pursuit approach

    Juan Lucas Bali;Graciela Boente;David E. Tyler;Jane-Ling Wang

Frequent Co-Authors

Hannu Oja
Hannu Oja University of Turku
Peter Meer
Peter Meer Rutgers, The State University of New Jersey
Visa Koivunen
Visa Koivunen Aalto University
Dorin Comaniciu
Dorin Comaniciu Siemens (United States)
Jane-Ling Wang
Jane-Ling Wang University of California, Davis
Hernando Ombao
Hernando Ombao King Abdullah University of Science and Technology
Ami Wiesel
Ami Wiesel Hebrew University of Jerusalem
Victor J. Yohai
Victor J. Yohai University of Buenos Aires

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