| Year |
Citation |
Score |
| 2019 |
Bhadra A, Datta J, Polson NG, Willard BT. Lasso Meets Horseshoe: A Survey Statistical Science. 34: 405-427. DOI: 10.1214/19-Sts700 |
0.344 |
|
| 2019 |
Polson NG, Sokolov V. Bayesian regularization: From Tikhonov to horseshoe Wiley Interdisciplinary Reviews: Computational Statistics. 11. DOI: 10.1002/Wics.1463 |
0.357 |
|
| 2019 |
Polson NG, Sun L. Bayesian l0‐regularized least squares Applied Stochastic Models in Business and Industry. 35: 717-731. DOI: 10.1002/Asmb.2381 |
0.381 |
|
| 2018 |
Polson N, Sokolov V. Bayesian Particle Tracking of Traffic Flows Ieee Transactions On Intelligent Transportation Systems. 19: 345-356. DOI: 10.1109/Tits.2017.2650947 |
0.324 |
|
| 2018 |
Warty SP, Lopes HF, Polson NG. Rejoinder to “Sequential Bayesian learning for stochastic volatility with variance-gamma jumps in returns” Reply to the discussions by Nalini Ravishanker and Refik Soyer: Reply to Discussions by Ravishanker and Soyer Applied Stochastic Models in Business and Industry. 34: 484-485. DOI: 10.1002/Asmb.2374 |
0.324 |
|
| 2018 |
Warty SP, Lopes HF, Polson NG. Sequential Bayesian learning for stochastic volatility with variance‐gamma jumps in returns Applied Stochastic Models in Business and Industry. 34: 460-479. DOI: 10.1002/Asmb.2258 |
0.403 |
|
| 2017 |
Polson NG, Sokolov V. Deep Learning: A Bayesian Perspective Bayesian Analysis. 12: 1275-1304. DOI: 10.1214/17-Ba1082 |
0.379 |
|
| 2017 |
Singpurwalla ND, Polson NG, Soyer R. From Least Squares to Signal Processing and Particle Filtering Technometrics. 60: 146-160. DOI: 10.1080/00401706.2017.1341341 |
0.339 |
|
| 2016 |
Heaton JB, Polson N, Witte JH. Deep Learning for Finance: Deep Portfolios Applied Stochastic Models in Business and Industry. 33: 3-12. DOI: 10.2139/Ssrn.2838013 |
0.352 |
|
| 2016 |
Feng G, Polson NG, Xu J. The Market for English Premier League (EPL) Odds Journal of Quantitative Analysis in Sports. 12: 167-178. DOI: 10.1515/Jqas-2016-0039 |
0.329 |
|
| 2016 |
Bhadra A, Datta J, Polson NG, Willard BT. Default Bayesian analysis with global-local shrinkage priors Biometrika. 103: 955-969. DOI: 10.1093/Biomet/Asw041 |
0.303 |
|
| 2016 |
Lopes HF, Polson NG. Particle Learning for Fat-Tailed Distributions Econometric Reviews. 1-26. DOI: 10.1080/07474938.2015.1092809 |
0.404 |
|
| 2015 |
Polson NG, Stern HS. The implied volatility of a sports game Journal of Quantitative Analysis in Sports. 11: 145-153. DOI: 10.1515/Jqas-2014-0095 |
0.345 |
|
| 2015 |
Polson NG, Scott JG, Willard BT. Proximal algorithms in statistics and machine learning Statistical Science. 30: 559-581. DOI: 10.1214/15-Sts530 |
0.304 |
|
| 2015 |
Polson N, Sokolov V. Bayesian analysis of traffic flow on interstate I-55: The LWR model The Annals of Applied Statistics. 9: 1864-1888. DOI: 10.1214/15-Aoas853 |
0.375 |
|
| 2015 |
Polson NG, Scott JG. Mixtures, envelopes and hierarchical duality Journal of the Royal Statistical Society. Series B: Statistical Methodology. DOI: 10.1111/Rssb.12130 |
0.393 |
|
| 2015 |
Brian V, Gron A, Polson NG. Bayesian estimation of nonlinear equilibrium models with random coefficients Applied Stochastic Models in Business and Industry. 31: 435-456. DOI: 10.1002/Asmb.2036 |
0.419 |
|
| 2014 |
Ekin T, Polson NG, Soyer R. Augmented Markov chain Monte Carlo simulation for two-stage stochastic programs with recourse Decision Analysis. 11: 250-264. DOI: 10.1287/Deca.2014.0303 |
0.342 |
|
| 2014 |
Polson NG, Scott JG, Windle J. The Bayesian bridge Journal of the Royal Statistical Society. Series B: Statistical Methodology. 76: 713-733. DOI: 10.1111/Rssb.12042 |
0.412 |
|
| 2014 |
Johannes M, Korteweg A, Polson N. Sequential Learning, Predictability, and Optimal Portfolio Returns: Sequential Learning, Predictability, and Optimal Portfolio Returns Journal of Finance. 69: 611-644. DOI: 10.1111/Jofi.12121 |
0.302 |
|
| 2014 |
Lopes HF, Polson NG. Bayesian Instrumental Variables: Priors and Likelihoods Econometric Reviews. 33: 100-121. DOI: 10.1080/07474938.2013.807146 |
0.351 |
|
| 2014 |
Korsos L, Polson NG. Analyzing risky choices: Q-learning for Deal-No-Deal Applied Stochastic Models in Business and Industry. 30: 258-270. DOI: 10.1002/Asmb.1971 |
0.324 |
|
| 2013 |
Johannes MS, Korteweg AG, Polson N. Sequential Learning, Predictability, and Optimal Portfolio Returns Journal of Finance. 69: 611-644. DOI: 10.2139/Ssrn.1108905 |
0.439 |
|
| 2013 |
Polson NG, Scott JG. Data augmentation for non-Gaussian regression models using variance-mean mixtures Biometrika. 100: 459-471. DOI: 10.1093/Biomet/Ass081 |
0.367 |
|
| 2013 |
Polson NG, Scott JG, Windle J. Bayesian inference for logistic models using Pólya-Gamma latent variables Journal of the American Statistical Association. 108: 1339-1349. DOI: 10.1080/01621459.2013.829001 |
0.384 |
|
| 2012 |
Dukic V, Lopes HF, Polson NG. Tracking Epidemics With Google Flu Trends Data and a State-Space SEIR Model. Journal of the American Statistical Association. 107: 1410-1426. PMID 37583443 DOI: 10.1080/01621459.2012.713876 |
0.365 |
|
| 2012 |
Polson NG, Scott JG. On the half-cauchy prior for a global scale parameter Bayesian Analysis. 7: 887-902. DOI: 10.1214/12-Ba730 |
0.401 |
|
| 2012 |
Gramacy RB, Polson NG. Simulation-based regularized logistic regression Bayesian Analysis. 7: 567-590. DOI: 10.1214/12-Ba719 |
0.401 |
|
| 2012 |
Lopes HF, Polson NG, Carvalho CM. Bayesian statistics with a smile: A resampling-sampling perspective Brazilian Journal of Probability and Statistics. 26: 358-371. DOI: 10.1214/11-Bjps144 |
0.396 |
|
| 2012 |
Polson NG, Scott JG. Local shrinkage rules, Lévy processes and regularized regression Journal of the Royal Statistical Society. Series B: Statistical Methodology. 74: 287-311. DOI: 10.1111/J.1467-9868.2011.01015.X |
0.333 |
|
| 2012 |
Gron A, Jørgensen BN, Polson NG. Optimal portfolio choice and stochastic volatility Applied Stochastic Models in Business and Industry. 28: 1-15. DOI: 10.1002/Asmb.898 |
0.34 |
|
| 2011 |
Polson NG, Scott SL. Data augmentation for support vector machines Bayesian Analysis. 6: 1-24. DOI: 10.1214/11-Ba601 |
0.383 |
|
| 2011 |
Gramacy RB, Polson NG. Particle learning of gaussian process models for sequential design and optimization Journal of Computational and Graphical Statistics. 20: 102-118. DOI: 10.1198/Jcgs.2010.09171 |
0.365 |
|
| 2011 |
Taddy MA, Gramacy RB, Polson NG. Dynamic trees for learning and design Journal of the American Statistical Association. 106: 109-123. DOI: 10.1198/Jasa.2011.Ap09769 |
0.396 |
|
| 2011 |
Zantedeschi D, Damien P, Polson NG. Predictive macro-finance with dynamic partition models Journal of the American Statistical Association. 106: 427-439. DOI: 10.1198/Jasa.2011.Ap09732 |
0.377 |
|
| 2011 |
Polson NG, Sorensen M. A simulation-based approach to stochastic dynamic programming Applied Stochastic Models in Business and Industry. 27: 151-163. DOI: 10.1002/Asmb.896 |
0.341 |
|
| 2010 |
Polson NG, Scott JG. Good, great, or lucky? Screening for firms with sustained superior performance using heavy-tailed priors Annals of Applied Statistics. 4: 161-185. DOI: 10.1214/11-Aoas512 |
0.341 |
|
| 2010 |
Carvalho CM, Johannes MS, Lopes HF, Polson NG. Particle Learning and Smoothing Statistical Science. 25: 88-106. DOI: 10.1214/10-Sts325 |
0.333 |
|
| 2010 |
Carvalho CM, Lopes HF, Polson NG, Taddy MA. Particle learning for general mixtures Bayesian Analysis. 5: 709-740. DOI: 10.1214/10-Ba525 |
0.35 |
|
| 2010 |
Carvalho CM, Polson NG, Scott JG. The horseshoe estimator for sparse signals Biometrika. 97: 465-480. DOI: 10.1093/Biomet/Asq017 |
0.399 |
|
| 2009 |
Johannes MS, Polson NG, Stroud JR. Optimal filtering of jump diffusions: Extracting latent states from asset prices Review of Financial Studies. 22: 2759-2799. DOI: 10.1093/Rfs/Hhn110 |
0.384 |
|
| 2008 |
Polson NG, Stroud JR, Müller P. Practical filtering with sequential parameter learning Journal of the Royal Statistical Society. Series B: Statistical Methodology. 70: 413-428. DOI: 10.1111/J.1467-9868.2007.00642.X |
0.408 |
|
| 2007 |
Jacquier E, Johannes M, Polson N. MCMC maximum likelihood for latent state models Journal of Econometrics. 137: 615-640. DOI: 10.1016/J.Jeconom.2005.11.017 |
0.425 |
|
| 2004 |
Jacquier E, Polson NG, Rossi PE. Bayesian analysis of stochastic volatility models with fat-tails and correlated errors Journal of Econometrics. 122: 185-212. DOI: 10.1016/J.Jeconom.2003.09.001 |
0.409 |
|
| 2003 |
Eraker B, Johannes M, Polson N. The Impact of Jumps in Volatility and Returns Journal of Finance. 58: 1269-1300. DOI: 10.2139/Ssrn.249764 |
0.688 |
|
| 2003 |
Stroud JR, Müller P, Polson NG. Nonlinear state-space models with state-dependent variances Journal of the American Statistical Association. 98: 377-386. DOI: 10.1198/016214503000161 |
0.369 |
|
| 2002 |
Jacquier E, Polson NG, Rossi PE. Bayesian analysis of stochastic volatility models Journal of Business and Economic Statistics. 20: 69-87. DOI: 10.1198/073500102753410408 |
0.45 |
|
| 2000 |
Polson NG, Tew BV. Bayesian portfolio selection: An empirical analysis of the S&P 500 index 1970-1996 Journal of Business and Economic Statistics. 18: 164-173. DOI: 10.1080/07350015.2000.10524859 |
0.413 |
|
| 2000 |
McCulloch RE, Polson NG, Rossi PE. A Bayesian analysis of the multinomial probit model with fully identified parameters Journal of Econometrics. 99: 173-193. DOI: 10.1016/S0304-4076(00)00034-8 |
0.362 |
|
| 1999 |
Polson NG, Yasumoto J. Investing in Leveraged Index Funds The Journal of Risk Finance. 1: 41-51. DOI: 10.1108/Eb022936 |
0.336 |
|
| 1996 |
Carota C, Parmigiani G, Polson NG. Diagnostic Measures for Model Criticism Journal of the American Statistical Association. 91: 753-762. DOI: 10.1080/01621459.1996.10476943 |
0.373 |
|
| 1994 |
Jacquier E, Polson NG, Rossi PE. Bayesian Analysis of Stochastic Volatility Models: Comments: Reply Journal of Business & Economic Statistics. 12: 413-417. DOI: 10.2307/1392209 |
0.376 |
|
| 1994 |
Roberts GO, Polson NG. On the Geometric Convergence of the Gibbs Sampler Journal of the Royal Statistical Society Series B-Methodological. 56: 377-384. DOI: 10.1111/J.2517-6161.1994.Tb01986.X |
0.353 |
|
| 1994 |
Polson NG, Roberts GO. Bayes factors for discrete observations from diffusion processes Biometrika. 81: 11-26. DOI: 10.1093/Biomet/81.1.11 |
0.332 |
|
| 1992 |
Polson NG. On the Expected Amount of Information from a Non‐Linear Model Journal of the Royal Statistical Society Series B-Methodological. 54: 889-895. DOI: 10.1111/J.2517-6161.1992.Tb01460.X |
0.362 |
|
| 1992 |
Carlin BP, Polson NG, Stoffer DS. A monte carlo approach to nonnormal and nonlinear state–space modeling Journal of the American Statistical Association. 87: 493-500. DOI: 10.1080/01621459.1992.10475231 |
0.34 |
|
| 1991 |
Carlin BP, Polson NG. Inference for nonconjugate Bayesian Models using the Gibbs sampler Canadian Journal of Statistics. 19: 399-405. DOI: 10.2307/3315430 |
0.357 |
|
| 1991 |
Polson NG. A representation of the posterior mean for a location model Biometrika. 78: 426-430. DOI: 10.1093/Biomet/78.2.426 |
0.397 |
|
| 1990 |
Polson N, Wasserman L. Prior distributions for the bivariate binomial Biometrika. 77: 901-904. DOI: 10.1093/Biomet/77.4.901 |
0.365 |
|
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