Optimal scaling for various Metropolis-Hastings algorithms GO Roberts, JS Rosenthal Statistical science 16 (4), 351-367, 2001 | 1522 | 2001 |
Examples of adaptive MCMC GO Roberts, JS Rosenthal Journal of computational and graphical statistics 18 (2), 349-367, 2009 | 1268 | 2009 |
General state space Markov chains and MCMC algorithms GO Roberts, JS Rosenthal | 1010 | 2004 |
Optimal scaling of discrete approximations to Langevin diffusions GO Roberts, JS Rosenthal Journal of the Royal Statistical Society: Series B (Statistical Methodology …, 1998 | 826 | 1998 |
Minorization conditions and convergence rates for Markov chain Monte Carlo JS Rosenthal Journal of the American Statistical Association 90 (430), 558-566, 1995 | 626 | 1995 |
Coupling and ergodicity of adaptive Markov chain Monte Carlo algorithms GO Roberts, JS Rosenthal Journal of applied probability 44 (2), 458-475, 2007 | 548 | 2007 |
On adaptive markov chain monte carlo algorithms YF Atchadé, JS Rosenthal Bernoulli 11 (5), 815-828, 2005 | 462 | 2005 |
First Look At Rigorous Probability Theory, A JS Rosenthal World Scientific Publishing Company, 2006 | 450 | 2006 |
Link analysis ranking: algorithms, theory, and experiments A Borodin, GO Roberts, JS Rosenthal, P Tsaparas ACM Transactions on Internet Technology (TOIT) 5 (1), 231-297, 2005 | 423 | 2005 |
Geometric ergodicity and hybrid Markov chains G Roberts, J Rosenthal | 405 | 1997 |
Finding authorities and hubs from link structures on the world wide web A Borodin, GO Roberts, JS Rosenthal, P Tsaparas Proceedings of the 10th international conference on World Wide Web, 415-429, 2001 | 389 | 2001 |
Finding generators for Markov chains via empirical transition matrices, with applications to credit ratings RB Israel, JS Rosenthal, JZ Wei Mathematical finance 11 (2), 245-265, 2001 | 385 | 2001 |
Optimal proposal distributions and adaptive MCMC JS Rosenthal Handbook of Markov Chain Monte Carlo 4 (10.1201), 2011 | 367 | 2011 |
Probability and statistics: The science of uncertainty MJ Evans, JS Rosenthal Macmillan, 2010 | 296* | 2010 |
Convergence rates for Markov chains JS Rosenthal Siam Review 37 (3), 387-405, 1995 | 276 | 1995 |
On the efficiency of pseudo-marginal random walk Metropolis algorithms C Sherlock, AH Thiery, GO Roberts, JS Rosenthal | 214 | 2015 |
Parallel computing and Monte Carlo algorithms JS Rosenthal Far east journal of theoretical statistics 4 (2), 207-236, 2000 | 201 | 2000 |
Markov-chain Monte Carlo: some practical implications of theoretical results GO Roberts, JS Rosenthal The Canadian Journal of Statistics/La Revue Canadienne de Statistique, 5-20, 1998 | 195 | 1998 |
Convergence of slice sampler Markov chains GO Roberts, JS Rosenthal Journal of the Royal Statistical Society Series B: Statistical Methodology …, 1999 | 178 | 1999 |
Active learning strategies in advanced mathematics classes JS Rosenthal Studies in Higher Education 20 (2), 223-228, 1995 | 161 | 1995 |
Learn from thy neighbor: Parallel-chain and regional adaptive MCMC RV Craiu, J Rosenthal, C Yang Journal of the American Statistical Association 104 (488), 1454-1466, 2009 | 156 | 2009 |
On the applicability of regenerative simulation in Markov chain Monte Carlo JP Hobert, GL Jones, B Presnell, JS Rosenthal Biometrika 89 (4), 731-743, 2002 | 149 | 2002 |
Harris recurrence of Metropolis-within-Gibbs and trans-dimensional Markov chains GO Roberts, JS Rosenthal | 141 | 2006 |
Towards optimal scaling of Metropolis-coupled Markov chain Monte Carlo YF Atchadé, GO Roberts, JS Rosenthal Statistics and Computing 21, 555-568, 2011 | 132 | 2011 |
Meetings with costly participation MJ Osborne, JS Rosenthal, MA Turner American Economic Review 90 (4), 927-943, 2000 | 124 | 2000 |
Capitalism's growth imperative MJ Gordon, JS Rosenthal Cambridge Journal of Economics 27 (1), 25-48, 2003 | 121 | 2003 |
Scaling limits for the transient phase of local Metropolis–Hastings algorithms OF Christensen, GO Roberts, JS Rosenthal Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2005 | 119 | 2005 |
Rates of convergence for Gibbs sampling for variance component models JS Rosenthal The Annals of Statistics 23 (3), 740-761, 1995 | 117 | 1995 |
Predicting university students’ academic success and major using random forests C Beaulac, JS Rosenthal Research in Higher Education 60, 1048-1064, 2019 | 115 | 2019 |
Quantitative bounds on convergence of time-inhomogeneous Markov chains R Douc, E Moulines, JS Rosenthal Annals of Applied Probability, 1643-1665, 2004 | 107 | 2004 |
Probabilidad y estadística: la ciencia de la incertidumbre MJ Evans Reverté, 2018 | 104 | 2018 |
Quantitative convergence rates of Markov chains: A simple account J Rosenthal | 101 | 2002 |
Analysis of the Gibbs sampler for a model related to James-Stein estimators JS Rosenthal Statistics and Computing 6, 269-275, 1996 | 99 | 1996 |
Extension of Fill's perfect rejection sampling algorithm to general chains JA Fill, M Machida, DJ Murdoch, JS Rosenthal Random Structures & Algorithms 17 (3‐4), 290-316, 2000 | 96 | 2000 |
Random rotations: characters and random walks on SO (N) JS Rosenthal The Annals of Probability, 398-423, 1994 | 96 | 1994 |
Bayesian computation via markov chain monte carlo RV Craiu, JS Rosenthal Annual Review of Statistics and Its Application 1, 179-201, 2014 | 92 | 2014 |
Struck by lightning: The curious world of probabilities JS Rosenthal National Academies Press, 2006 | 79 | 2006 |
A simulation approach to convergence rates for Markov chain Monte Carlo algorithms MK Cowles, JS Rosenthal Statistics and Computing 8, 115-124, 1998 | 79 | 1998 |
Judicial ghostwriting: authorship on the Supreme Court JS Rosenthal, AH Yoon Cornell L. Rev. 96, 1307, 2010 | 77 | 2010 |
Markov chains and de‐initializing processes GO Roberts, JS Rosenthal Scandinavian Journal of Statistics 28 (3), 489-504, 2001 | 76 | 2001 |
Adaptive Gibbs samplers and related MCMC methods K Łatuszyński, GO Roberts, JS Rosenthal | 73 | 2013 |
On the containment condition for adaptive Markov chain Monte Carlo algorithms Y Bai, GO Roberts, JS Rosenthal University of Warwick. Centre for Research in Statistical Methodology 2009 (15), 2009 | 73 | 2009 |
Bayesian models for sparse regression analysis of high dimensional data S Richardson, L Bottolo, JS Rosenthal Bayesian statistics 9, 539-569, 2010 | 71 | 2010 |
Possible biases induced by MCMC convergence diagnostics MK Cowles, GO Roberts, JS Rosenthal Journal of Statistical Computation and Simulation 64 (1), 87-104, 1999 | 71 | 1999 |
Moment conditions for a sequence with negative drift to be uniformly bounded in Lr R Pemantle, JS Rosenthal Stochastic Processes and their Applications 82 (1), 143-155, 1999 | 71 | 1999 |
DISCUSSION ON THE MEETING ON THE GIBBS SAMPLER AND OTHER MARKOV CHAIN-MONTE CARLO METHODS P Clifford, C Jennison, J Wakefield, D Phillips, A Frigessi, AJ Gray, ... J ROY STAT SOC B MET 55 (1), 53-102, 1993 | 71 | 1993 |
Optimal scaling of Metropolis algorithms: Heading toward general target distributions M Bédard, JS Rosenthal Canadian Journal of Statistics 36 (4), 483-503, 2008 | 70 | 2008 |
Convergence properties of perturbed Markov chains GO Roberts, JS Rosenthal, PO Schwartz Journal of applied probability 35 (1), 1-11, 1998 | 68 | 1998 |
Quantitative bounds for convergence rates of continuous time Markov processes G Roberts, J Rosenthal | 65 | 1996 |
Criminal trajectories and risk factors in a Canadian sample of offenders AK Ward, DM Day, I Bevc, Y Sun, JS Rosenthal, T Duchesne Criminal Justice and Behavior 37 (11), 1278-1300, 2010 | 64 | 2010 |
Rates of convergence for everywhere-positive Markov chains JR Baxter, JS Rosenthal Statistics & probability letters 22 (4), 333-338, 1995 | 61 | 1995 |
Handbook of markov chain monte carlo CJ Geyer, C Robert, G Casella, Y Fan, SA Sisson, JS Rosenthal Chapman Hall/CRC 3, 592, 2011 | 60 | 2011 |
Variance bounding Markov chains GO Roberts, JS Rosenthal | 60 | 2008 |
Rates of convergence for data augmentation on finite sample spaces JS Rosenthal The Annals of Applied Probability 3 (3), 819-839, 1993 | 60 | 1993 |
Group-based criminal trajectory analysis using cross-validation criteria JD Nielsen, JS Rosenthal, Y Sun, DM Day, I Bevc, T Duchesne Communications in Statistics-Theory and Methods 43 (20), 4337-4356, 2014 | 55 | 2014 |
Asymptotic variance and convergence rates of nearly-periodic Markov chain Monte Carlo algorithms JS Rosenthal Journal of the American Statistical Association 98 (461), 169-177, 2003 | 55 | 2003 |
On the geometric ergodicity of hybrid samplers G Fort, E Moulines, GO Roberts, JS Rosenthal Journal of Applied Probability 40 (1), 123-146, 2003 | 55 | 2003 |
AMCMC: An R interface for adaptive MCMC JS Rosenthal Computational Statistics & Data Analysis 51 (12), 5467-5470, 2007 | 52 | 2007 |
Grades and incentives: assessing competing grade point average measures and postgraduate outcomes MA Bailey, JS Rosenthal, AH Yoon Studies in Higher Education 41 (9), 1548-1562, 2016 | 49 | 2016 |
Monty hall, monty fall, monty crawl JS Rosenthal Math Horizons 16 (1), 5-7, 2008 | 46 | 2008 |
Faithful couplings of Markov chains: now equals forever JS Rosenthal Advances in Applied Mathematics 18 (3), 372-381, 1997 | 46 | 1997 |
On variance conditions for Markov chain CLTs O Haggstrom, J Rosenthal | 43 | 2007 |
Two convergence properties of hybrid samplers GO Roberts, JS Rosenthal The Annals of Applied Probability 8 (2), 397-407, 1998 | 43 | 1998 |
Shift-coupling and convergence rates of ergodic averages GO Roberts, JS Rosenthal Stochastic Models 13 (1), 147-165, 1997 | 43 | 1997 |
Convergence of conditional Metropolis-Hastings samplers GL Jones, GO Roberts, JS Rosenthal Advances in Applied Probability 46 (2), 422-445, 2014 | 41 | 2014 |
Minimising MCMC variance via diffusion limits, with an application to simulated tempering GO Roberts, JS Rosenthal | 41 | 2014 |
A review of asymptotic convergence for general state space Markov chains JS Rosenthal Far East J. Theor. Stat 5 (1), 37-50, 2001 | 39 | 2001 |
Decrypting classical cipher text using Markov chain Monte Carlo J Chen, JS Rosenthal Statistics and Computing 22 (2), 397-413, 2012 | 38 | 2012 |
The polar slice sampler GO Roberts, JS Rosenthal Stochastic Models 18 (2), 257-280, 2002 | 38 | 2002 |
Quantitative bounds for geometric convergence rates of Markov chains R Douc, E Moulines, J Rosenthal Ann. Appl. Probab 14 (4), 1643-1665, 2004 | 37 | 2004 |
Positional targets for lingual consonants defined using electromagnetic articulography Y Yunusova, JS Rosenthal, K Rudy, M Baljko, J Daskalogiannakis The Journal of the Acoustical Society of America 132 (2), 1027-1038, 2012 | 36 | 2012 |
Markov chain convergence: From finite to infinite JS Rosenthal Stochastic processes and their Applications 62 (1), 55-72, 1996 | 35 | 1996 |
Long-term follow-up of criminal activity with adjudicated youth in Ontario: Identifying offence trajectories and predictors/correlates of trajectory group membership DM Day, JD Nielsen, AK Ward, Y Sun, JS Rosenthal, T Duchesne, I Bevc, ... Canadian Journal of Criminology and Criminal Justice 54 (4), 377-413, 2012 | 34 | 2012 |
Efficient use of exact samples DJ Murdoch, JS Rosenthal Statistics and Computing 10, 237-243, 2000 | 34 | 2000 |
Complexity results for MCMC derived from quantitative bounds J Yang, JS Rosenthal arXiv preprint arXiv:1708.00829, 2017 | 31* | 2017 |
Detecting multiple authorship of United States Supreme Court legal decisions using function words JS Rosenthal, AH Yoon The Annals of applied statistics, 283-308, 2011 | 30 | 2011 |
Downweighting tightly knit communities in world wide web rankings GO Roberts, JS Rosenthal Advances and Applications in Statistics (ADAS) 3, 199-216, 2003 | 30 | 2003 |
Weight-preserving simulated tempering NG Tawn, GO Roberts, JS Rosenthal Statistics and Computing 30 (1), 27-41, 2020 | 29 | 2020 |
Complexity bounds for Markov chain Monte Carlo algorithms via diffusion limits GO Roberts, JS Rosenthal Journal of Applied Probability 53 (2), 410-420, 2016 | 29 | 2016 |
On convergence rates of Gibbs samplers for uniform distributions GO Roberts, JS Rosenthal Annals of Applied Probability, 1291-1302, 1998 | 29 | 1998 |
One-shot coupling for certain stochastic recursive sequences GO Roberts, JS Rosenthal Stochastic processes and their applications 99 (2), 195-208, 2002 | 27 | 2002 |
Optimal scaling of random-walk metropolis algorithms on general target distributions J Yang, GO Roberts, JS Rosenthal Stochastic Processes and their Applications 130 (10), 6094-6132, 2020 | 26 | 2020 |
Stability of adversarial Markov chains, with an application to adaptive MCMC algorithms RV Craiu, L Gray, K Łatuszyński, N Madras, GO Roberts, JS Rosenthal | 26 | 2015 |
BEST: A decision tree algorithm that handles missing values C Beaulac, JS Rosenthal Computational Statistics 35 (3), 1001-1026, 2020 | 25 | 2020 |
Perfect forward simulation via simulated tempering SP Brooks, Y Fan, JS Rosenthal Communications in Statistics-Simulation and Computation 35 (3), 683-713, 2006 | 25 | 2006 |
Small and pseudo-small sets for Markov chains GO Roberts, JS Rosenthal Stochastic Models 17 (2), 121-145, 2001 | 25 | 2001 |
Likelihood inflating sampling algorithm R Entezari, RV Craiu, JS Rosenthal Canadian Journal of Statistics 46 (1), 147-175, 2018 | 24 | 2018 |
Extremal indices, geometric ergodicity of Markov chains, and MCMC GO Roberts, JS Rosenthal, J Segers, B Sousa Extremes 9, 213-229, 2006 | 23 | 2006 |
Statistical inference and computational efficiency for spatial infectious disease models with plantation data PE Brown, F Chimard, A Remorov, JS Rosenthal, X Wang Journal of the Royal Statistical Society Series C: Applied Statistics 63 (3 …, 2014 | 21 | 2014 |
Quantitative non-geometric convergence bounds for independence samplers GO Roberts, JS Rosenthal Methodology and Computing in Applied Probability 13 (2), 391-403, 2011 | 21 | 2011 |
A mathematical analysis of the Sleeping Beauty problem JS Rosenthal The Mathematical Intelligencer 31 (3), 32-37, 2009 | 20 | 2009 |
Change and continuity in criminal offending: Criminal trajectories of the “Toronto” sample DM Day, I Bevc, F Theodor, JS Rosenthal, T Duchesne Ontario: Ministry of Children and Youth Services, 2008 | 19 | 2008 |
On the collapsibility of lifetime regression models T Duchesne, JS Rosenthal Advances in Applied Probability 35 (3), 755-772, 2003 | 19 | 2003 |
A note on geometric ergodicity and floating-point roundoff error L Breyer, GO Roberts, JS Rosenthal Statistics & probability letters 53 (2), 123-127, 2001 | 19 | 2001 |
Dimension-free mixing for high-dimensional Bayesian variable selection Q Zhou, J Yang, D Vats, GO Roberts, JS Rosenthal Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2022 | 18 | 2022 |
Ergodicity of Markov processes via nonstandard analysis H Duanmu, J Rosenthal, W Weiss American Mathematical Society 273 (1342), 2021 | 18 | 2021 |
Surprising convergence properties of some simple Gibbs samplers under various scans GO Roberts, JS Rosenthal International Journal of Statistics and Probability 5 (1), 51-60, 2015 | 18 | 2015 |
Jump Markov chains and rejection-free Metropolis algorithms JS Rosenthal, A Dote, K Dabiri, H Tamura, S Chen, A Sheikholeslami Computational Statistics, 1-23, 2021 | 17 | 2021 |
Markov chain Monte Carlo algorithms: Theory and practice JS Rosenthal Monte Carlo and Quasi-Monte Carlo Methods 2008, 157-169, 2009 | 16 | 2009 |
Detection of bulbar ALS using a comprehensive speech assessment battery Y Yunusova, JS Rosenthal, JR Green, S Shellikeri, P Rong, J Wang, ... Proc. of the International Workshop on Models and Analysis of Vocal …, 2013 | 15 | 2013 |