| Forecastable component analysis G Goerg International conference on machine learning, 64-72, 2013 | 128 | 2013 |
| The Lambert Way to Gaussianize Heavy-Tailed Data with the Inverse of Tukey’s h Transformation as a Special Case GM Goerg The Scientific World Journal 2014, 2014 | 104* | 2014 |
| Lambert W random variables—a new family of generalized skewed distributions with applications to risk estimation GM Goerg Annals of Applied Statistics 5 (3), 2197-2230, 2011 | 100 | 2011 |
| LambertW: probabilistic models to analyze and gaussianize heavy-tailed, skewed data GM Goerg R package: version 0.6.9, 2020 | 72* | 2020 |
| Pathogen mutation modeled by competition between site and bond percolation L Hébert-Dufresne, O Patterson-Lomba, GM Goerg, BM Althouse Physical review letters 110 (10), 108103, 2013 | 39 | 2013 |
| Mixed LICORS: A nonparametric algorithm for predictive state reconstruction G Goerg, C Shalizi Artificial Intelligence and Statistics, 289-297, 2013 | 20 | 2013 |
| LICORS: Light cone reconstruction of states for non-parametric forecasting of spatio-temporal systems GM Goerg, CR Shalizi arXiv preprint arXiv:1206.2398, 2012 | 18 | 2012 |
| The timing and targeting of treatment in influenza pandemics influences the emergence of resistance in structured populations BM Althouse, O Patterson-Lomba, GM Goerg, L Hebert-Dufresne PLoS computational biology 9 (2), e1002912, 2013 | 17 | 2013 |
| Optimizing treatment regimes to hinder antiviral resistance in influenza across time scales O Patterson-Lomba, BM Althouse, GM Goerg, L Hebert-Dufresne PloS one 8 (3), e59529, 2013 | 14 | 2013 |
| Foreca: An r package for forecastable component analysis GM Goerg R package version 0.2 4, 2016 | 11 | 2016 |
| Improving topic clustering on search queries with word co-occurrence and bipartite graph coclustering J Kong, A Scott, GM Goerg | 11* | 2016 |
| LICORS: Light cone reconstruction of states—predictive state estimation from spatio-temporal data GM Goerg R package version 0.2. 0, 2013 | 8 | 2013 |
| Testing for white noise against locally stationary alternatives GM Goerg Statistical Analysis and Data Mining: The ASA Data Science Journal 5 (6 …, 2012 | 8 | 2012 |
| afmtools: Estimation, Diagnostic and Forecasting Functions for ARFIMA models J Contreras-Reyes, GM Goerg, W Palma R package available at CRAN, 2011 | 8 | 2011 |
| When to combine television with online campaigns: Cost savings versus extended reach GM Goerg, C Best, S Shobowale, N Remy, J Koehler Journal of Advertising Research 57 (3), 283-304, 2017 | 7 | 2017 |
| A nonparametric frequency domain EM algorithm for time series classification with applications to spike sorting and macro‐economics GM Goerg Statistical Analysis and Data Mining: The ASA Data Science Journal 4 (6 …, 2011 | 6 | 2011 |
| Escaping the poverty trap: modeling the interplay between economic growth and the ecology of infectious disease GM Goerg, O Patterson-Lomba, L HÊbert-Dufresne, BM Althouse arXiv preprint arXiv:1311.4079, 2013 | 5 | 2013 |
| How many millenials visit youtube? estimating unobserved events from incomplete panel data conditioned on demographic covariates GM Goerg, Y Jin, N Remy, J Koehler Technical report, Google Inc, 2015 | 4 | 2015 |
| Time series analysis of long memory versus structural breaks: A time-varying memory approach GM Goerg (No Title) 1, 120, 2010 | 4 | 2010 |
| How many people visit youtube? imputing missing events in panels with excess zeros GM Goerg, Y Jin, N Remy, J Koehler Proceedings of 30th International Workshop on Statistical Modelling, Linz …, 2015 | 3 | 2015 |
Ben AlthouseUniversity of Washington; New Mexico State UniversityVerified email at uw.edu
