| Year |
Citation |
Score |
| 2022 |
Cramer EY, Ray EL, Lopez VK, Bracher J, Brennen A, Castro Rivadeneira AJ, Gerding A, Gneiting T, House KH, Huang Y, Jayawardena D, Kanji AH, Khandelwal A, Le K, Mühlemann A, ... ... Wasserman L, et al. Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States. Proceedings of the National Academy of Sciences of the United States of America. 119: e2113561119. PMID 35394862 DOI: 10.1073/pnas.2113561119 |
0.693 |
|
| 2021 |
McDonald DJ, Bien J, Green A, Hu AJ, DeFries N, Hyun S, Oliveira NL, Sharpnack J, Tang J, Tibshirani R, Ventura V, Wasserman L, Tibshirani RJ. Can auxiliary indicators improve COVID-19 forecasting and hotspot prediction? Proceedings of the National Academy of Sciences of the United States of America. 118. PMID 34903655 DOI: 10.1073/pnas.2111453118 |
0.375 |
|
| 2021 |
Reinhart A, Brooks L, Jahja M, Rumack A, Tang J, Agrawal S, Al Saeed W, Arnold T, Basu A, Bien J, Cabrera ÁA, Chin A, Chua EJ, Clark B, Colquhoun S, ... ... Wasserman L, et al. An open repository of real-time COVID-19 indicators. Proceedings of the National Academy of Sciences of the United States of America. 118. PMID 34903654 DOI: 10.1073/pnas.2111452118 |
0.679 |
|
| 2020 |
Wasserman L, Ramdas A, Balakrishnan S. Universal inference. Proceedings of the National Academy of Sciences of the United States of America. PMID 32631986 DOI: 10.1073/Pnas.1922664117 |
0.67 |
|
| 2020 |
Politsch CA, Cisewski-Kehe J, Croft RAC, Wasserman L. Trend filtering – I. A modern statistical tool for time-domain astronomy and astronomical spectroscopy Monthly Notices of the Royal Astronomical Society. 492: 4005-4018. DOI: 10.1093/Mnras/Staa106 |
0.308 |
|
| 2019 |
Rinaldo A, Tibshirani RJ, Wasserman L. Comment: Statistical Inference from a Predictive Perspective Statistical Science. 34: 599-603. DOI: 10.1214/19-Sts748 |
0.337 |
|
| 2018 |
Tibshirani RJ, Rinaldo A, Tibshirani R, Wasserman L. Uniform asymptotic inference and the bootstrap after model selection The Annals of Statistics. 46: 1255-1287. DOI: 10.1214/17-Aos1584 |
0.518 |
|
| 2018 |
Lei J, G’Sell M, Rinaldo A, Tibshirani RJ, Wasserman L. Distribution-Free Predictive Inference for Regression Journal of the American Statistical Association. 113: 1094-1111. DOI: 10.1080/01621459.2017.1307116 |
0.362 |
|
| 2016 |
Chen YC, Genovese CR, Tibshirani RJ, Wasserman L. Nonparametric modal regression Annals of Statistics. 44: 489-514. DOI: 10.1214/15-Aos1373 |
0.349 |
|
| 2015 |
Robins JM, Hernán MA, Wasserman L. Discussion of "On Bayesian estimation of marginal structural models". Biometrics. 71: 296-9. PMID 25652314 DOI: 10.1111/Biom.12273 |
0.319 |
|
| 2015 |
Chazal F, Fasy B, Lecci F, Rinaldo A, Singh A, Wasserman L. On the Bootstrap for Persistence Diagrams and Landscapes Modeling and Analysis of Information Systems. 20: 111-120. DOI: 10.18255/1818-1015-2013-6-111-120 |
0.588 |
|
| 2015 |
Chen Y, Ho S, Freeman PE, Genovese CR, Wasserman L. Cosmic web reconstruction through density ridges: method and algorithm Monthly Notices of the Royal Astronomical Society. 454: 1140-1156. DOI: 10.1093/Mnras/Stv1996 |
0.33 |
|
| 2015 |
Lei J, Rinaldo A, Wasserman L. A conformal prediction approach to explore functional data Annals of Mathematics and Artificial Intelligence. 74: 29-43. DOI: 10.1007/S10472-013-9366-6 |
0.323 |
|
| 2014 |
Stolzer M, Wasserman L, Durand D. Robustness of birth-death and gain models for inferring evolutionary events. Bmc Genomics. 15: S9. PMID 25572914 DOI: 10.1186/1471-2164-15-S6-S9 |
0.371 |
|
| 2014 |
Fasy BT, Lecci F, Rinaldo A, Wasserman L, Balakrishnan S, Singh A. Confidence sets for persistence diagrams Annals of Statistics. 42: 2301-2339. DOI: 10.1214/14-Aos1252 |
0.628 |
|
| 2014 |
Lei J, Wasserman L. Distribution-free prediction bands for non-parametric regression Journal of the Royal Statistical Society. Series B: Statistical Methodology. 76: 71-96. DOI: 10.1111/Rssb.12021 |
0.332 |
|
| 2014 |
Cisewski J, Croft RAC, Freeman PE, Genovese CR, Khandai N, Ozbek M, Wasserman L. Non-parametric 3D map of the intergalactic medium using the lyman-alpha forest Monthly Notices of the Royal Astronomical Society. 440: 2599-2609. DOI: 10.1093/Mnras/Stu475 |
0.309 |
|
| 2013 |
Lei J, Robins J, Wasserman L. Distribution Free Prediction Sets. Journal of the American Statistical Association. 108: 278-287. PMID 25237208 DOI: 10.1080/01621459.2012.751873 |
0.353 |
|
| 2013 |
Azizyan M, Singh A, Wasserman L. Density-sensitive semisupervised inference Annals of Statistics. 41: 751-771. DOI: 10.1214/13-Aos1092 |
0.61 |
|
| 2012 |
Zhao T, Liu H, Roeder K, Lafferty J, Wasserman L. The huge Package for High-dimensional Undirected Graph Estimation in R. Journal of Machine Learning Research : Jmlr. 13: 1059-1062. PMID 26834510 |
0.363 |
|
| 2012 |
Lafferty J, Liu H, Wasserman L. Sparse nonparametric graphical models Statistical Science. 27: 519-537. DOI: 10.1214/12-Sts391 |
0.441 |
|
| 2012 |
Liu H, Han F, Yuan M, Lafferty J, Wasserman L. High Dimensional Semiparametric Gaussian Copula Graphical Models Annals of Statistics. 40: 2293-2326. DOI: 10.1214/12-Aos1037 |
0.47 |
|
| 2012 |
Genovese CR, Perone-Pacifico M, Isabella V, Wasserman L. The geometry of nonparametric filament estimation Journal of the American Statistical Association. 107: 788-799. DOI: 10.1080/01621459.2012.682527 |
0.315 |
|
| 2011 |
Percival D, Roeder K, Rosenfeld R, Wasserman L. STRUCTURED, SPARSE REGRESSION WITH APPLICATION TO HIV DRUG RESISTANCE. The Annals of Applied Statistics. 5: 628-644. PMID 21892380 DOI: 10.1214/10-Aoas428 |
0.551 |
|
| 2010 |
Liu H, Roeder K, Wasserman L. Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models. Advances in Neural Information Processing Systems. 24: 1432-1440. PMID 25152607 |
0.404 |
|
| 2010 |
Lee AB, Wasserman L. Spectral connectivity analysis Journal of the American Statistical Association. 105: 1241-1255. DOI: 10.1198/Jasa.2010.Tm09754 |
0.347 |
|
| 2010 |
Wasserman L, Zhou S. A statistical framework for differential privacy Journal of the American Statistical Association. 105: 375-389. DOI: 10.1198/Jasa.2009.Tm08651 |
0.333 |
|
| 2010 |
Zhou S, Lafferty J, Wasserman L. Time varying undirected graphs Machine Learning. 80: 295-319. DOI: 10.1007/S10994-010-5180-0 |
0.322 |
|
| 2009 |
Wasserman L, Roeder K. HIGH DIMENSIONAL VARIABLE SELECTION. Annals of Statistics. 37: 2178-2201. PMID 19784398 DOI: 10.1214/08-Aos646 |
0.303 |
|
| 2009 |
Genovese CR, Perone-Pacifico M, Verdinelli I, Wasserman L. On the path density of a gradient field Annals of Statistics. 37: 3236-3271. DOI: 10.1214/08-Aos671 |
0.359 |
|
| 2009 |
Genovese C, Freeman P, Wasserman L, Nichol R, Miller C. Inference for the dark energy equation of state using Type Ia Supernova data Annals of Applied Statistics. 3: 144-178. DOI: 10.1214/08-Aoas229 |
0.313 |
|
| 2009 |
Ravikumar P, Lafferty J, Liu H, Wasserman L. Sparse additive models Journal of the Royal Statistical Society. Series B: Statistical Methodology. 71: 1009-1030. DOI: 10.1111/J.1467-9868.2009.00718.X |
0.512 |
|
| 2009 |
Zhou S, Lafferty J, Wasserman L. Compressed and privacy-sensitive sparse regression Ieee Transactions On Information Theory. 55: 846-866. DOI: 10.1109/Tit.2008.2009605 |
0.388 |
|
| 2009 |
Ravikumar P, Liu H, Lafferty J, Wasserman L. SpAM: Sparse additive models Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference. |
0.303 |
|
| 2008 |
Lafferty J, Wasserman L. Rodeo: Sparse, greedy nonparametric regression Annals of Statistics. 36: 28-63. DOI: 10.1214/009053607000000811 |
0.34 |
|
| 2007 |
Bryan B, Schneider J, Miller CJ, Nichol RC, Genovese C, Wasserman L. Mapping the cosmological confidence ball surface Astrophysical Journal. 665: 25-41. DOI: 10.1086/518999 |
0.335 |
|
| 2007 |
Perone Pacifico M, Genovese C, Verdinelli I, Wasserman L. Scan clustering: A false discovery approach Journal of Multivariate Analysis. 98: 1441-1469. DOI: 10.1016/J.Jmva.2006.11.011 |
0.315 |
|
| 2007 |
Lafferty J, Wasserman L. Comments on: Nonparametric inference with generalized likelihood ratio tests : N Test. 16: 453-455. DOI: 10.1007/S11749-007-0084-4 |
0.334 |
|
| 2006 |
Brame R, Nagin DS, Wasserman L. Exploring Some Analytical Characteristics of Finite Mixture Models Journal of Quantitative Criminology. 22: 31-59. DOI: 10.1007/S10940-005-9001-8 |
0.359 |
|
| 2005 |
Genovese CR, Wasserman L. Confidence sets for nonparametric wavelet regression Annals of Statistics. 33: 698-729. DOI: 10.1214/009053605000000011 |
0.311 |
|
| 2004 |
Genovese CR, Miller CJ, Nichol RC, Arjunwadkar M, Wasserman L. Nonparametric inference for the cosmic microwave background Statistical Science. 19: 308-321. DOI: 10.1214/088342304000000161 |
0.364 |
|
| 2004 |
Genovese C, Wasserman L. A stochastic process approach to false discovery control Annals of Statistics. 32: 1035-1061. DOI: 10.1214/009053604000000283 |
0.305 |
|
| 2004 |
Perone Pacifico M, Genovese C, Verdinelli I, Wasserman L. False discovery control for random fields Journal of the American Statistical Association. 99: 1002-1014. DOI: 10.1198/0162145000001655 |
0.321 |
|
| 2003 |
Devlin B, Roeder K, Wasserman L. Analysis of multilocus models of association. Genetic Epidemiology. 25: 36-47. PMID 12813725 DOI: 10.1002/Gepi.10237 |
0.351 |
|
| 2003 |
Tzeng J, Byerley W, Devlin B, Roeder K, Wasserman L. Outlier Detection and False Discovery Rates for Whole-Genome DNA Matching Journal of the American Statistical Association. 98: 236-246. DOI: 10.1198/016214503388619256 |
0.324 |
|
| 2003 |
Robins JM, Scheines R, Spirtes P, Wasserman L. Uniform consistency in causal inference Biometrika. 90: 491-515. DOI: 10.1093/Biomet/90.3.491 |
0.334 |
|
| 2002 |
Petrone S, Wasserman L. Consistency of Bernstein polynomial posteriors Journal of the Royal Statistical Society Series B-Statistical Methodology. 64: 79-100. DOI: 10.1111/1467-9868.00326 |
0.314 |
|
| 2002 |
Miller CJ, Nichol RC, Genovese C, Wasserman L. A nonparametric analysis of the cosmic microwave background power spectrum Astrophysical Journal. 565. DOI: 10.1086/339366 |
0.336 |
|
| 2001 |
Seltman H, Greenhouse J, Wasserman L. Bayesian model selection: Analysis of a survival model with a surviving fraction Statistics in Medicine. 20: 1681-1691. PMID 11391695 DOI: 10.1002/Sim.779 |
0.335 |
|
| 2001 |
Miller CJ, Genovese C, Nichol RC, Wasserman L, Connolly A, Reichart D, Hopkins A, Schneider J, Moore A. Controlling the False-Discovery Rate in Astrophysical Data Analysis The Astronomical Journal. 122: 3492-3505. DOI: 10.1086/324109 |
0.336 |
|
| 2000 |
Wasserman L. Bayesian model selection and model averaging Journal of Mathematical Psychology. 44: 92-107. PMID 10733859 DOI: 10.1006/Jmps.1999.1278 |
0.35 |
|
| 2000 |
Genovese CR, Wasserman L. Rates Of Convergence For The Gaussian Mixture Sieve Annals of Statistics. 28: 1105-1127. DOI: 10.1214/Aos/1015956709 |
0.308 |
|
| 2000 |
Wasserman L. Asymptotic inference for mixture models by using data-dependent priors Journal of the Royal Statistical Society Series B-Statistical Methodology. 62: 159-180. DOI: 10.1111/1467-9868.00226 |
0.384 |
|
| 1999 |
Carroll RJ, Roeder K, Wasserman L. Flexible parametric measurement error models. Biometrics. 55: 44-54. PMID 11318178 DOI: 10.1111/J.0006-341X.1999.00044.X |
0.358 |
|
| 1998 |
Verdinelli I, Wasserman L. Bayesian goodness-of-fit testing using infinite-dimensional exponential families Annals of Statistics. 26: 1215-1241. DOI: 10.1214/Aos/1024691240 |
0.324 |
|
| 1998 |
Cohen J, Nagin D, Wallstrom G, Wasserman L. Hierarchical Bayesian Analysis of Arrest Rates Journal of the American Statistical Association. 93: 1260-1270. DOI: 10.1080/01621459.1998.10473787 |
0.386 |
|
| 1997 |
Herron T, Seidenfeld T, Wasserman L. Divisive Conditioning: Further Results on Dilation Philosophy of Science. 64: 411-444. DOI: 10.1086/392559 |
0.32 |
|
| 1997 |
Diciccio TJ, Kass RE, Raftery A, Wasserman L. Computing Bayes factors by combining simulation and asymptotic approximations Journal of the American Statistical Association. 92: 903-915. DOI: 10.1080/01621459.1997.10474045 |
0.317 |
|
| 1997 |
Roeder K, Wasserman L. Practical Bayesian Density Estimation Using Mixtures of Normals Journal of the American Statistical Association. 92: 894-902. DOI: 10.1080/01621459.1997.10474044 |
0.346 |
|
| 1996 |
Shaw JEH, Genz A, Monahan J, Schervish MJ, Wasserman L, Wolfinger R, Evans M, Swartz T. Comments on: "Methods for approximating integrals in statistics with special emphasis on Bayesian integration problems" by M. Evans and T. SwartzCommentCommentCommentRejoinder Statistical Science. 11: 54-64. DOI: 10.1214/Ss/1032209664 |
0.471 |
|
| 1996 |
Kass RE, Wasserman L. The selection of prior distributions by formal rules Journal of the American Statistical Association. 91: 1343-1370. DOI: 10.1080/01621459.1996.10477003 |
0.318 |
|
| 1996 |
Wasserman L. The conflict between improper priors and robustness Journal of Statistical Planning and Inference. 52: 1-15. DOI: 10.1016/0378-3758(95)00109-3 |
0.311 |
|
| 1995 |
Kass RE, Wasserman L. A reference Bayesian test for nested hypotheses and its relationship to the schwarz criterion Journal of the American Statistical Association. 90: 928-934. DOI: 10.1080/01621459.1995.10476592 |
0.309 |
|
| 1994 |
Tibshirani R, Wasserman L. Some aspects of the reparametrization of statistical models Canadian Journal of Statistics. 22: 163-173. DOI: 10.2307/3315831 |
0.526 |
|
| 1992 |
Wasserman L, Kadane JB. Computing Bounds on Expectations Journal of the American Statistical Association. 87: 516-522. DOI: 10.1080/01621459.1992.10475234 |
0.322 |
|
| 1991 |
Lavine M, Wasserman L, Wolpert RL. Bayesian inference with specified prior marginals Journal of the American Statistical Association. 86: 964-971. DOI: 10.1080/01621459.1991.10475139 |
0.316 |
|
| 1991 |
Verdinelli I, Wasserman L. Bayesian analysis of outlier problems using the Gibbs sampler Statistics and Computing. 1: 105-117. DOI: 10.1007/Bf01889985 |
0.367 |
|
| 1990 |
Polson N, Wasserman L. Prior distributions for the bivariate binomial Biometrika. 77: 901-904. DOI: 10.1093/Biomet/77.4.901 |
0.342 |
|
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