| Finite mixture and Markov switching models S Frühwirth-Schnatter Springer, 2006 | 2246 | 2006 |
| Data augmentation and dynamic linear models S Frühwirth‐Schnatter Journal of time series analysis 15 (2), 183-202, 1994 | 1232 | 1994 |
| Markov chain Monte Carlo estimation of classical and dynamic switching and mixture models S Frühwirth-Schnatter Journal of the American Statistical Association 96 (453), 194-209, 2001 | 633 | 2001 |
| Ancillarity-sufficiency interweaving strategy (ASIS) for boosting MCMC estimation of stochastic volatility models G Kastner, S Frühwirth-Schnatter Computational Statistics & Data Analysis 76, 408-423, 2014 | 394 | 2014 |
| Stochastic model specification search for Gaussian and partial non-Gaussian state space models S Frühwirth-Schnatter, H Wagner Journal of Econometrics 154 (1), 85-100, 2010 | 295 | 2010 |
| Bayesian inference for finite mixtures of univariate and multivariate skew-normal and skew-t distributions S Frühwirth-Schnatter, S Pyne Biostatistics 11 (2), 317-336, 2010 | 255 | 2010 |
| Estimating marginal likelihoods for mixture and Markov switching models using bridge sampling techniques S Frühwirth‐Schnatter The Econometrics Journal 7 (1), 143-167, 2004 | 254 | 2004 |
| Model-based clustering of multiple time series S Fröhwirth-Schnatter, S Kaufmann Journal of Business & Economic Statistics 26 (1), 78-89, 2008 | 233 | 2008 |
| Model-based clustering based on sparse finite Gaussian mixtures G Malsiner-Walli, S Frühwirth-Schnatter, B Grün Statistics and computing 26 (1), 303-324, 2016 | 215 | 2016 |
| Achieving shrinkage in a time-varying parameter model framework A Bitto, S Frühwirth-Schnatter Journal of Econometrics 210 (1), 75-97, 2019 | 208 | 2019 |
| Handbook of mixture analysis S Fruhwirth-Schnatter, G Celeux, CP Robert CRC press, 2019 | 194 | 2019 |
| Bayesian exploratory factor analysis G Conti, S Frühwirth-Schnatter, JJ Heckman, R Piatek Journal of econometrics 183 (1), 31-57, 2014 | 178 | 2014 |
| Auxiliary mixture sampling for parameter-driven models of time series of counts with applications to state space modelling S Frühwirth-Schnatter, H Wagner Biometrika 93 (4), 827-841, 2006 | 172 | 2006 |
| Efficient Bayesian inference for multivariate factor stochastic volatility models G Kastner, S Frühwirth-Schnatter, HF Lopes Journal of Computational and Graphical Statistics 26 (4), 905-917, 2017 | 148 | 2017 |
| Data augmentation and MCMC for binary and multinomial logit models S Frühwirth-Schnatter, R Frühwirth Statistical modelling and regression structures: Festschrift in honour of …, 2010 | 147 | 2010 |
| Auxiliary mixture sampling with applications to logistic models S Fruehwirth-Schnatter, R Frühwirth Computational Statistics & Data Analysis 51 (7), 3509-3528, 2007 | 142 | 2007 |
| Improved auxiliary mixture sampling for hierarchical models of non-Gaussian data S Frühwirth-Schnatter, R Frühwirth, L Held, H Rue Statistics and Computing 19, 479-492, 2009 | 119 | 2009 |
| Bayesian model discrimination and Bayes factors for linear Gaussian state space models S Frühwirth‐Schnatter Journal of the Royal Statistical Society: Series B (Methodological) 57 (1 …, 1995 | 110 | 1995 |
| From here to infinity: sparse finite versus Dirichlet process mixtures in model-based clustering S Frühwirth-Schnatter, G Malsiner-Walli Advances in data analysis and classification 13, 33-64, 2019 | 99 | 2019 |
| On fuzzy Bayesian inference S Frühwirth-Schnatter Fuzzy sets and systems 60 (1), 41-58, 1993 | 99 | 1993 |
