Nicholas Polson

Q: What Schools Are Affiliated With Nicholas Polson

Nicholas Polson is affiliated with the following schools: University of Oxford, University of Nottingham, University of Chicago

Nicholas Polson's AcademicInfluence.com Rankings

Nicholas Polson

Mathematics

#5836

World Rank

#8176

Historical Rank

#1850

USA Rank

Statistics

#463

World Rank

#537

Historical Rank

#135

USA Rank

mathematics Degrees

Download Badge

Mathematics

Nicholas Polson's Degrees

PhD Statistics University of Chicago
Masters Statistics University of Chicago

Why Is Nicholas Polson Influential?

(Suggest an Edit or Addition)

According to Wikipedia, Nicholas Polson is a British statistician who is a professor of econometrics and statistics at the University of Chicago Booth School of Business. His works are primarily in Bayesian statistics, Markov chain Monte Carlo and Sequential Monte Carlo, . Polson was educated at Worcester College, Oxford University and the University of Nottingham where his PhD supervisor was Adrian Smith.

(See a Problem?)

Nicholas Polson's Published Works

Number of citations in a given year to any of this author's works

Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author

Published Works

Bayesian Analysis of Stochastic Volatility Models (1994) (1725)
The Impact of Jumps in Volatility and Returns (2000) (1122)
The horseshoe estimator for sparse signals (2010) (1120)
Bayesian Inference for Logistic Models Using Pólya–Gamma Latent Variables (2012) (807)
Deep learning for short-term traffic flow prediction (2016) (681)
A Monte Carlo Approach to Nonnormal and Nonlinear State-Space Modeling (1992) (630)
Bayesian analysis of stochastic volatility models with fat-tails and correlated errors (2004) (627)
Particle Filtering (2006) (615)
Shrink Globally, Act Locally: Sparse Bayesian Regularization and Prediction (2012) (432)
Handling Sparsity via the Horseshoe (2009) (418)
Evidence for and the Impact of Jumps in Volatility and Returns (2001) (371)
On the half-cauchy prior for a global scale parameter (2011) (340)
A Bayesian analysis of the multinomial probit model with fully identified parameters (2000) (298)
Deep Learning for Finance: Deep Portfolios (2016) (271)
Bayesian Analysis of Stochastic Volatility Models: Comments: Reply (1994) (270)
Optimal Filtering of Jump Diffusions: Extracting Latent States from Asset Prices (2009) (202)
MCMC Methods for Continuous-Time Financial Econometrics (2003) (200)
Deep learning for finance: deep portfolios: J. B. HEATON, N. G. POLSON AND J. H. WITTE (2017) (192)
Data augmentation for support vector machines (2011) (186)
Tracking Epidemics With Google Flu Trends Data and a State-Space SEIR Model (2012) (156)
MCMC Methods for Financial Econometrics (2002) (155)
The Horseshoe+ Estimator of Ultra-Sparse Signals (2015) (131)
Inference for nonconjugate Bayesian Models using the Gibbs sampler (1991) (131)
On the Geometric Convergence of the Gibbs Sampler (1994) (130)
The Bayesian bridge (2011) (126)
Deep Learning in Finance (2016) (126)
Proximal Algorithms in Statistics and Machine Learning (2015) (123)
Local shrinkage rules, Lévy processes and regularized regression (2010) (121)
Dynamic Trees for Learning and Design (2009) (121)
Particle Learning of Gaussian Process Models for Sequential Design and Optimization (2009) (115)
Sequential Learning, Predictability, and Optimal Portfolio Returns (2013) (107)
MCMC maximum likelihood for latent state models (2007) (99)
Stochastic Volatility: Univariate and Multivariate Extensions (1999) (97)
Deep Learning: A Bayesian Perspective (2017) (93)
Lasso Meets Horseshoe: A Survey (2017) (93)
Sequential Learning, Predictability, and Optimal Portfolio Returns: Sequential Learning, Predictability, and Optimal Portfolio Returns (2014) (92)
Bayesian Portfolio Selection: An Empirical Analysis of the S&P 500 Index 1970–1996 (2000) (91)
Sampling from log-concave distributions (1994) (84)
Practical filtering with sequential parameter learning (2008) (76)
Particle learning for general mixtures (2010) (76)
Simulation-based Regularized Logistic Regression (2010) (64)
Models and Priors for Multivariate Stochastic Volatility (1995) (61)
Posterior Concentration for Sparse Deep Learning (2018) (59)
Diagnostic Measures for Model Criticism (1996) (58)
Deep learning for spatio‐temporal modeling: Dynamic traffic flows and high frequency trading (2017) (51)
Deep Learning for Predicting Asset Returns (2018) (48)
Nonlinear State-Space Models With State-Dependent Variances (2003) (48)
Default Bayesian Analysis with Global-Local Shrinkage Priors (2015) (46)
Bayesian Theory and Applications (2013) (45)
Corporate Credit Spreads under Parameter Uncertainty (2009) (41)
Bayesian Instrumental Variables: Priors and Likelihoods (2014) (40)
Bayes factors for discrete observations from diffusion processes (1994) (38)
Data augmentation for non-Gaussian regression models using variance-mean mixtures (2011) (36)
Sampling P olya-Gamma random variates: alternate and approximate techniques (2014) (32)
Deep Learning in Characteristics-Sorted Factor Models (2018) (31)
Deep Learning Predictors for Traffic Flows (2016) (30)
Nonlinear Filtering of Stochastic Differential Equations with Jumps (2002) (29)
Sequential Bayesian Analysis of Multivariate Count Data (2016) (29)
State Space and Unobserved Component Models: Practical filtering for stochastic volatility models (2004) (29)
Sequential Parameter Estimation in Stochastic Volatility Models with Jumps (2006) (28)
Bayesian analysis of traffic flow on interstate I-55: The LWR model (2014) (28)
Particle Learning for Sequential Bayesian Computation (2012) (27)
Good, great, or lucky? Screening for firms with sustained superior performance using heavy-tailed priors (2010) (27)
Bayesian Particle Tracking of Traffic Flows (2014) (25)
Deep Portfolio Theory (2016) (23)
A representation of the posterior mean for a location model (1991) (20)
Predictive Macro-Finance With Dynamic Partition Models (2011) (20)
Prior distributions for the bivariate binomial (1990) (19)
A Note on the Residual Entropy Function (1993) (19)
Particle Filtering and Parameter Learning (2007) (18)
Weighted Bayesian bootstrap for scalable posterior distributions (2020) (18)
Mixtures, envelopes and hierarchical duality (2014) (18)
Bayesian regularization: From Tikhonov to horseshoe (2019) (17)
Rejoinder to ‘Deep learning for finance: deep portfolios’: REJOINDER (2017) (17)
Optimal portfolio choice and stochastic volatility (2012) (17)
Augmented Markov Chain Monte Carlo Simulation for Two-Stage Stochastic Programs with Recourse (2014) (16)
Bayesian l 0 ‐regularized least squares (2017) (16)
Bayesian Analysis of a Stochastic Volatility Model with Leverage Effect and Fat Tails (2001) (16)
Asset Allocation in Finance: A Bayesian Perspective (2011) (15)
Prediction Risk for the Horseshoe Regression (2016) (14)
The Horseshoe-Like Regularization for Feature Subset Selection (2019) (14)
Explosive Volatility: A Model of Financial Contagion (2011) (14)
From Least Squares to Signal Processing and Particle Filtering (2017) (14)
On the Expected Amount of Information from a Non‐Linear Model (1992) (13)
Default Bayesian analysis for multi-way tables: a data-augmentation approach (2011) (13)
Alternative Global – Local Shrinkage Priors Using Hypergeometric – Beta Mixtures (2009) (13)
Large-scale simultaneous testing with hypergeometric inverted-beta priors (2010) (13)
Lasso Meets Horseshoe (2017) (13)
Statistical sparsity (2017) (12)
Exact Particle Filtering and Parameter Learning (2006) (12)
Horseshoe Regularization for Machine Learning in Complex and Deep Models (2019) (11)
Why Indexing Works (2015) (11)
Vertical-likelihood Monte Carlo (2014) (10)
Weighted Bayesian Bootstrap for Scalable Bayes (2018) (10)
Chess AI: Competing Paradigms for Machine Intelligence (2021) (10)
Particle Learning for Fat-Tailed Distributions (2016) (9)
Why indexing works: Wny Indexing Works (2017) (9)
A utility based approach to information for stochastic differential equations (1993) (9)
Augmented nested sampling for stochastic programs with recourse and endogenous uncertainty (2017) (9)
Deep Learning Partial Least Squares (2021) (9)
Bayesian Methods In Finance (2011) (9)
Horseshoe Regularisation for Machine Learning in Complex and Deep Models 1 (2019) (9)
The implied volatility of a sports game (2015) (9)
A deconvolution path for mixtures (2015) (8)
A simulation-based approach to stochastic dynamic programming (2011) (8)
Horseshoe Regularization for Feature Subset Selection (2017) (8)
Sparse Bayes estimation in non-Gaussian models via data augmentation (2011) (8)
Sequential learning , predictive regressions , and optimal p ortfolio returns PRELIMINARY (2009) (8)
Sequential Bayesian learning for stochastic volatility with variance‐gamma jumps in returns (2018) (8)
Deep learning: Computational aspects (2018) (7)
Bayesian statistics with a smile: A resampling-sampling perspective (2012) (7)
[Practical Markov Chain Monte Carlo]: Comment (1992) (7)
CHAPTER 13 – MCMC Methods for Continuous-Time Financial Econometrics (2010) (7)
Bayesian perspectives on statistical modelling (1988) (7)
Deep Factor Alpha (2018) (6)
Deep Learning Factor Alpha ∗ (2018) (6)
A Statistical Theory of Deep Learning via Proximal Splitting (2015) (6)
Split Sampling: Expectations, Normalisation and Rare Events (2012) (6)
Investing in Leveraged Index Funds (1999) (6)
The market for English Premier League (EPL) odds (2016) (5)
Tracking Epidemics with State-space SEIR and Google Flu Trends (2009) (5)
Particle Learning in Nonlinear Models using Slice Variables (2009) (5)
Simulation-based-Estimation in Portfolio Selection (2009) (5)
Deep Fundamental Factor Models (2019) (4)
A family of multivariate non‐gaussian time series models (2020) (4)
Augmented probability simulation for accelerated life test design (2017) (4)
Ineligibles and eligible non-participants as a double comparison group in regression-discontinuity designs (2018) (4)
Rejoinder: "Data augmentation for support vector machines" (2011) (4)
[RETRACTED] On Hilbert’s 8th problem (2017) (4)
Global-Local Mixtures (2016) (4)
Merging Two Cultures: Deep and Statistical Learning (2021) (4)
Prediction risk for global-local shrinkage regression (2016) (4)
Volatility Timing and Portfolio Returns (2000) (4)
Analysis of economic data with multiscale spatio-temporal models (2018) (4)
Quantile Filtering and Learning (2009) (4)
Scalable Data Augmentation for Deep Learning (2019) (4)
A Bellman View of Jesse Livermore (2014) (3)
Regularizing Bayesian predictive regressions (2016) (3)
Optimisation via Slice Sampling (2012) (3)
Sequential Bayesian Analysis of Multivariate Poisson Count Data (2016) (3)
Short Communication: Deep Fundamental Factor Models (2020) (3)
Bayesian hypothesis testing: Redux (2018) (3)
Deep Partial Least Squares for IV Regression (2022) (3)
Diagnostic Measures for Model (1993) (3)
Markov Chain Monte Carlo (2009) (3)
Testing the parametric form of the volatility in continuous time diffusion models - a stochastic process approach (2019) (3)
Sequential Inference for Nonlinear Models using Slice Variables (2009) (2)
Maximum Expected Utility via MCMC (2006) (2)
Analyzing Risky Choices: Q-learning for Deal-No-Deal (2011) (2)
Bayesian Inference for Polya Inverse Gamma Models. (2019) (2)
CHAPTER ON BAYESIAN INFERENCE FOR STOCHASTIC VOLATILITY MODELING (2010) (2)
van Dantzig Pairs, Wald Couples and Hadamard Factorisation. (2018) (2)
Bayesian Computation in Finance (2010) (2)
Bayesian estimation of nonlinear equilibrium models with random coefficients (2015) (2)
Riemann's Xi-function: A GGC representation (2018) (2)
A Bayesian Decision Theoretic Characterization of Poisson Processes (1991) (1)
Bayesian Inference for Gamma Models (2021) (1)
Deep Learning (2018) (1)
Global-Local Mixtures: A Unifying Framework (2020) (1)
Sequential Bayesian Learning for Merton's Jump Model with Stochastic Volatility (2016) (1)
Particle Learning for Fat-tailed Distributions 1 (2010) (1)
Sparse Regularization in Marketing and Economics (2017) (1)
Deep Learning in Asset Pricing∗ (2019) (1)
Regularization via Data Augmentation (2011) (1)
Deep Partial Least Squares for Empirical Asset Pricing (2022) (1)
Correction: Sampling from Log-Concave Distributions (1994) (1)
Série Scientifique Scientific Series N o 95 s18 MODELS AND PRIORS FOR MULTIVARIATE STOCHASTIC VOLATILITY (1997) (0)
Where Will Yahoo! Stock Be in Five Years? (2000) (0)
Vertical-likelihood Monte Carlo Integration (2014) (0)
1 5 O ct 2 01 8 Riemann Hypothesis : a GGC factorisation (2018) (0)
Wald Couples and Hadamard Factorisation (2018) (0)
Distribution free specification tests of conditional models (2018) (0)
Data Augementation with Polya Inverse Gamma (2019) (0)
Testing for unit root processes in random coefficient autoregressive models (2019) (0)
Feature Selection for Personalized Policy Analysis (2022) (0)
Data Augmentation for Bayesian Deep Learning (2019) (0)
Internet Appendix for : Sequential Learning , Predictability , and Optimal Portfolio Returns (2013) (0)
Karpov's Queen Sacrifices and AI (2021) (0)
Iterative and Recursive Estimation in Structural Nonadaptive Models: Comment (2003) (0)
The Impact ofJumps inVolatility and Returns (2002) (0)
Discussion on ‘Adversarial risk analysis: Borel games’ (2011) (0)
PolyaGamma Sampling [R package BayesLogit version 2.1] (2019) (0)
Rejoinder to “Sequential Bayesian learning for stochastic volatility with variance-gamma jumps in returns” Reply to the discussions by Nalini Ravishanker and Refik Soyer (2018) (0)
Accelerated Life Tests That Maximize Shannon Information (1988) (0)
Retraction: On Hilbert’s 8th problem (2018) (0)
Bayesian Analysis of A General Class of Multivariate Exponential Family of State Space Models (2017) (0)
Computations in Bayesian Design of Life Tests (2009) (0)
Riemann Hypothesis: a GGC factorisation (2018) (0)
Exceedance as a measure of sparsity (2017) (0)
Adaptive consistent unit root tests based on autoregressive threshold model (2019) (0)
Nonparametric simultaneous testing for structural breaks (2020) (0)
Bayesian Design for Random Walk Barriers (2009) (0)
The Market for English Premier League Odds12 (2017) (0)
Inverse Probability Weighting: from Survey Sampling to Evidence Estimation (2022) (0)
Sequential Regression Trees for Learning and Design (2009) (0)
An empirical test for Eurozone contagion using an asset-pricing model with heavy-tailed stochastic volatility (2011) (0)

This paper list is powered by the following services:

Other Resources About Nicholas Polson

en.wikipedia.org

What Schools Are Affiliated With Nicholas Polson?

Nicholas Polson is affiliated with the following schools:

Nicholas Polson's Academic­Influence.com Rankings