World's Best Scientists 2026 revealed!

D-Index & Metrics

Mathematics

D-Index
49
Citations
19353
World Ranking
1111
National Ranking
511

Research.com Recognitions

  • 2006 - Fellow of the American Statistical Association (ASA)

Overview

Nicholas G. Polson is affiliated with the University of Chicago in the United States, focusing primarily on Computer Science research. Their work encompasses a range of subfields including Artificial Intelligence, Statistics and Probability, Management Science and Operations Research, Finance, and Signal Processing.

The scientist's research concentrates on several main topics such as Forecasting Techniques and Applications, Statistical Methods and Inference, Financial Risk and Volatility Modeling, Stock Market Forecasting Methods, Time Series Analysis and Forecasting, Bayesian Methods and Mixture Models, and Neural Networks and Applications.

Key recent publications by Nicholas G. Polson include:

  • Deep Learning in Characteristics-Sorted Factor Models, 2023, Journal of Financial and Quantitative Analysis
  • A family of multivariate non-gaussian time series models, 2020, Journal of Time Series Analysis
  • Merging two cultures: Deep and statistical learning, 2024, Wiley Interdisciplinary Reviews Computational Statistics
  • Bayesian Inference for Gamma Models, 2021, arXiv (Cornell University)
  • Global-Local Mixtures: A Unifying Framework, 2020, Sankhya A

Their frequent collaborators include Jyotishka Datta, Jianeng Xu, Anindya Bhadra, Vadim Sokolov, and Jingyu He.

Nicholas G. Polson has published extensively in various venues, with significant contributions to arXiv (Cornell University), where they have at least ten publications. Other venues include the Journal of Financial and Quantitative Analysis, Journal of Time Series Analysis, Wiley Interdisciplinary Reviews Computational Statistics, and Sankhya A.

In 2006, Nicholas G. Polson was awarded the status of Fellow of the American Statistical Association (ASA).

Best Publications

  • Bayesian Analysis of Stochastic Volatility Models

    Eric Jacquier;Nicholas G Polson;Peter E Rossi

  • The Impact of Jumps in Volatility and Returns

    Bjørn Eraker;Michael Johannes;Nicholas Polson

  • The horseshoe estimator for sparse signals

    Carlos Marinho Carvalho;Nicholas G. Polson;James G Scott

  • Bayesian Inference for Logistic Models Using Pólya–Gamma Latent Variables

    Nicholas G. Polson;James G. Scott;Jesse Windle

  • Deep learning for short-term traffic flow prediction

    Nicholas G. Polson;Vadim O. Sokolov

  • A Monte Carlo Approach to Nonnormal and Nonlinear State-Space Modeling

    Bradley P. Carlin;Nicholas G. Polson;David S. Stoffer

  • Bayesian analysis of stochastic volatility models with fat-tails and correlated errors

    Eric Jacquier;Nicholas G. Polson;Peter E. Rossi

  • Deep learning for finance: deep portfolios

    J. B. Heaton;N. G. Polson;J. H. Witte

  • The Impact of Jumps in Volatility and Returns

    Michael S. Johannes;Bjorn Eraker;Nick Polson

  • On the half-cauchy prior for a global scale parameter

    Nicholas G. Polson;James G. Scott

  • Shrink Globally, Act Locally: Sparse Bayesian Regularization and Prediction

    Nicholas G. Polson;James G. Scott;Bertrand Clarke;C. Severinski

  • Particle Learning and Smoothing

    Carlos M. Carvalho;Michael Johannes;Hedibert Freitas Lopes;Nicholas Polson

  • A Bayesian analysis of the multinomial probit model with fully identified parameters

    Robert E. McCulloch;Nicholas G. Polson;Peter E. Rossi

  • MCMC Methods for Continuous-Time Financial Econometrics

    Michael Johannes;Nicholas Polson

  • [Bayesian Analysis of Stochastic Volatility Models]: Reply

    Eric Jacquier;Nicholas G. Polson;Peter E. Rossi

  • Optimal Filtering of Jump Diffusions: Extracting Latent States from Asset Prices

    Michael S. Johannes;Nicholas G. Polson;Jonathan R. Stroud

  • Data augmentation for support vector machines

    Nicholas G. Polson;Steven L. Scott

  • Tracking Epidemics With Google Flu Trends Data and a State-Space SEIR Model

    Vanja Dukic;Hedibert F. Lopes;Nicholas G. Polson

  • On the Geometric Convergence of the Gibbs Sampler

    Gareth O. Roberts;Nicholas G. Polson

  • Inference for nonconjugate Bayesian Models using the Gibbs sampler

    Bradley P. Carlin;Nicholas G. Polson

  • Sequential Learning, Predictability, and Optimal Portfolio Returns

    Michael Johannes;Arthur Korteweg;Nicholas Polson

Frequent Co-Authors

Peter E. Rossi
Peter E. Rossi University of California, Los Angeles
Robert B. Gramacy
Robert B. Gramacy Virginia Tech
Gareth O. Roberts
Gareth O. Roberts University of Warwick
Nozer D. Singpurwalla
Nozer D. Singpurwalla George Washington University
Peter Müller
Peter Müller The University of Texas at Austin
Bradley P. Carlin
Bradley P. Carlin University of Minnesota
Peter McCullagh
Peter McCullagh University of Chicago
Ravi Kannan
Ravi Kannan Microsoft (United States)
Alan Frieze
Alan Frieze Carnegie Mellon University
Giovanni Parmigiani
Giovanni Parmigiani Harvard University

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