World's Best Scientists 2026 revealed!

D-Index & Metrics

Mathematics

D-Index
37
Citations
8762
World Ranking
2435
National Ranking
30

Sylvia Frühwirth-Schnatter publication distribution in Mathematics in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Mathematics in 2026. The highlighted bar marks where Sylvia Frühwirth-Schnatter sits on this spectrum.

42–46 publications: 3 scientists 47–51 publications: 5 scientists 52–56 publications: 7 scientists 57–61 publications: 20 scientists 62–66 publications: 14 scientists 67–71 publications: 25 scientists 72–76 publications: 19 scientists 77–81 publications: 35 scientists 82–86 publications: 50 scientists 87–91 publications: 60 scientists 92–96 publications: 86 scientists 97–101 publications: 84 scientists 102–106 publications: 83 scientists 107–111 publications: 90 scientists 112–116 publications: 99 scientists 117–121 publications: 90 scientists 122–126 publications: 91 scientists 127–131 publications: 109 scientists 132–136 publications: 110 scientists 137–141 publications: 98 scientists 142–146 publications: 112 scientists 147–151 publications: 102 scientists 152–156 publications: 88 scientists 157–161 publications: 106 scientists 162–166 publications: 83 scientists 167–171 publications: 102 scientists 172–176 publications: 77 scientists 177–181 publications: 81 scientists 182–186 publications: 78 scientists 187–191 publications: 71 scientists 192–196 publications: 92 scientists 197–201 publications: 64 scientists 202–206 publications: 69 scientists 207–211 publications: 64 scientists 212–216 publications: 62 scientists 217–221 publications: 58 scientists 222–226 publications: 53 scientists 227–231 publications: 50 scientists 232–236 publications: 46 scientists 237–241 publications: 46 scientists 242–246 publications: 46 scientists 247–251 publications: 43 scientists 252–256 publications: 29 scientists 257–261 publications: 45 scientists 262–266 publications: 30 scientists 267–271 publications: 33 scientists 272–276 publications: 34 scientists 277–281 publications: 30 scientists 282–286 publications: 31 scientists 287–291 publications: 21 scientists 292–296 publications: 34 scientists 297–301 publications: 26 scientists 302–306 publications: 10 scientists 307–311 publications: 17 scientists 312–316 publications: 23 scientists 317–321 publications: 13 scientists 322–326 publications: 16 scientists 327–331 publications: 26 scientists 332–336 publications: 13 scientists 337–341 publications: 13 scientists 342–346 publications: 16 scientists 347–351 publications: 17 scientists 352–356 publications: 12 scientists 357–361 publications: 18 scientists 362–366 publications: 18 scientists 367–371 publications: 9 scientists 372–376 publications: 11 scientists 377–381 publications: 8 scientists 382–386 publications: 8 scientists 387–391 publications: 9 scientists 392–396 publications: 9 scientists 397–401 publications: 8 scientists 402–406 publications: 11 scientists 407–411 publications: 6 scientists 412–416 publications: 6 scientists 417–421 publications: 9 scientists 422–426 publications: 8 scientists 427–431 publications: 5 scientists 432–436 publications: 8 scientists 437–441 publications: 8 scientists 442–446 publications: 4 scientists 447–451 publications: 4 scientists 452–456 publications: 4 scientists 457–461 publications: 2 scientists 462–466 publications: 2 scientists 467–471 publications: 4 scientists 472–476 publications: 3 scientists 477–481 publications: 3 scientists 482–486 publications: 6 scientists 487–491 publications: 3 scientists 492–496 publications: 5 scientists 497–501 publications: 5 scientists 502–506 publications: 1 scientists 507–511 publications: 6 scientists 512–516 publications: 4 scientists 517–521 publications: 1 scientists 522–526 publications: 3 scientists 527–531 publications: 1 scientists 532–536 publications: 4 scientists 537+ publications: 100 scientists
42 publications 537+

This scientist: 100 publications — 11th percentile

11% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 537 publications or more.

Sylvia Frühwirth-Schnatter D-index placement in Mathematics in 2026

The chart shows the D-index (discipline H-index) distribution of Mathematics scientists ranked by Research.com in 2026. The highlighted bar marks where Sylvia Frühwirth-Schnatter sits on this spectrum.

30 D-Index: 174 scientists 31 D-Index: 151 scientists 32 D-Index: 174 scientists 33 D-Index: 117 scientists 34 D-Index: 136 scientists 35 D-Index: 127 scientists 36 D-Index: 145 scientists 37 D-Index: 153 scientists 38 D-Index: 150 scientists 39 D-Index: 150 scientists 40 D-Index: 138 scientists 41 D-Index: 136 scientists 42 D-Index: 93 scientists 43 D-Index: 108 scientists 44 D-Index: 115 scientists 45 D-Index: 112 scientists 46 D-Index: 103 scientists 47 D-Index: 75 scientists 48 D-Index: 59 scientists 49 D-Index: 67 scientists 50 D-Index: 60 scientists 51 D-Index: 57 scientists 52 D-Index: 59 scientists 53 D-Index: 62 scientists 54 D-Index: 60 scientists 55 D-Index: 50 scientists 56 D-Index: 42 scientists 57 D-Index: 54 scientists 58 D-Index: 50 scientists 59 D-Index: 42 scientists 60 D-Index: 41 scientists 61 D-Index: 35 scientists 62 D-Index: 40 scientists 63 D-Index: 21 scientists 64 D-Index: 31 scientists 65 D-Index: 27 scientists 66 D-Index: 29 scientists 67 D-Index: 19 scientists 68 D-Index: 25 scientists 69 D-Index: 17 scientists 70 D-Index: 18 scientists 71 D-Index: 12 scientists 72 D-Index: 14 scientists 73 D-Index: 13 scientists 74 D-Index: 18 scientists 75 D-Index: 9 scientists 76 D-Index: 11 scientists 77 D-Index: 10 scientists 78 D-Index: 9 scientists 79 D-Index: 16 scientists 80 D-Index: 12 scientists 81 D-Index: 10 scientists 82 D-Index: 5 scientists 83 D-Index: 5 scientists 84 D-Index: 13 scientists 85 D-Index: 6 scientists 86+ D-Index: 99 scientists
30 D-Index 86+

This scientist: 37 D-Index — 33rd percentile

33% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 86 D-Index or more.

Overview

Sylvia Frühwirth-Schnatter is affiliated with the Vienna University of Economics and Business in Austria. Their research spans multiple fields and subfields, with a strong emphasis on mathematical and computational methodologies applied to economics and statistics.

The principal fields of study include:

  • Mathematics
  • Computer Science

Subfields particularly relevant to their work are:

  • Statistics and Probability
  • Artificial Intelligence
  • Economics and Econometrics
  • Management Science and Operations Research
  • General Economics, Econometrics and Finance

Their research topics focus predominantly on:

  • Bayesian Methods and Mixture Models
  • Statistical Methods and Bayesian Inference
  • Statistical Methods and Inference
  • Monetary Policy and Economic Impact
  • Forecasting Techniques and Applications
  • Spatial and Panel Data Analysis
  • Advanced Clustering Algorithms Research

Their recent scholarly output includes several papers published in recognized venues. Selected recent works are:

  • Sparse Bayesian Factor Analysis When the Number of Factors Is Unknown, 2024, Bayesian Analysis
  • Generalized Mixtures of Finite Mixtures and Telescoping Sampling, 2021, Bayesian Analysis
  • Shrinkage in the Time-Varying Parameter Model Framework Using the R Package shrinkTVP, 2021, Journal of Statistical Software
  • Spying on the prior of the number of data clusters and the partition distribution in Bayesian cluster analysis, 2022, Australian & New Zealand Journal of Statistics
  • Ultimate Pólya Gamma Samplers-Efficient MCMC for Possibly Imbalanced Binary and Categorical Data, 2023, Journal of the American Statistical Association

Publications appear frequently in venues such as:

  • arXiv (Cornell University)
  • Bayesian Analysis
  • Econometrics
  • Advances in Data Analysis and Classification
  • Journal of the American Statistical Association

Frequent collaborators in research projects include:

  • Gertraud Malsiner-Walli
  • Bettina Grün
  • Darjus Hosszejni
  • Helga Wagner
  • Hedibert F. Lopes

Best Publications

  • Finite Mixture and Markov Switching Models

    Sylvia Frühwirth-Schnatter

  • Data Augmentation and Dynamic Linear Models

    Sylvia Frühwirth-Schnatter

  • Markov chain Monte Carlo Estimation of Classical and Dynamic Switching and Mixture Models

    Sylvia Frühwirth-Schnatter

  • Ancillarity-Sufficiency Interweaving Strategy (ASIS) for Boosting MCMC Estimation of Stochastic Volatility Models

    Gregor Kastner;Sylvia Fruhwirth-Schnatter

  • Stochastic model specification search for Gaussian and partial non-Gaussian state space models

    Sylvia Frühwirth-Schnatter;Helga Wagner

  • Bayesian inference for finite mixtures of univariate and multivariate skew-normal and skew-t distributions.

    Sylvia Frühwirth-Schnatter;Saumyadipta Pyne

  • Estimating marginal likelihoods for mixture and Markov switching models using bridge sampling techniques

    Sylvia Frühwirth‐Schnatter

  • Handbook of Mixture Analysis

    Sylvia Fruhwirth-Schnatter;Gilles Celeux;Christian P. Robert;Christian P. Robert

  • Model-based clustering based on sparse finite Gaussian mixtures

    Gertraud Malsiner-Walli;Sylvia Frühwirth-Schnatter;Bettina Grün

  • Achieving shrinkage in a time-varying parameter model framework

    Angela Bitto;Sylvia Frühwirth-Schnatter

  • Efficient Bayesian Inference for Multivariate Factor Stochastic Volatility Models

    Gregor Kastner;Sylvia Frühwirth-Schnatter;Hedibert Freitas Lopes

  • Auxiliary mixture sampling for parameter-driven models of time series of counts with applications to state space modelling

    Sylvia FrüHwirth-Schnatter;Helga Wagner

  • Bayesian exploratory factor analysis

    Gabriella Conti;Sylvia Frühwirth-Schnatter;James J. Heckman;Rémi Piatek

  • Data Augmentation and MCMC for Binary and Multinomial Logit Models

    Sylvia Frühwirth-Schnatter;Rudolf Frühwirth

  • Auxiliary mixture sampling with applications to logistic models

    Sylvia Frühwirth-Schnatter;Rudolf Frühwirth

  • Improved auxiliary mixture sampling for hierarchical models of non-Gaussian data

    Sylvia Frühwirth-Schnatter;Rudolf Frühwirth;Leonhard Held;Håvard Rue

  • Bayesian Model Discrimination and Bayes Factors for Linear Gaussian State Space Models

    Sylvia Frühwirth-Schnatter

  • On fuzzy Bayesian inference

    Sylvia Frühwirth-Schnatter

  • Applied State Space Modelling of Non-Gaussian Time Series using Integration-based Kalman-filtering

    Sylvia Frühwirth-Schnatter

  • On statistically inference for fuzzy data with applications to descriptive statistics

    Sylvia Frühwirth-Schnatter

Frequent Co-Authors

Rudolf Winter-Ebmer
Rudolf Winter-Ebmer Johannes Kepler University of Linz
Christian P. Robert
Christian P. Robert Paris Dauphine University
Chang-Jin Kim
Chang-Jin Kim University of Washington
Charles R. Nelson
Charles R. Nelson University of Washington
Herman K. van Dijk
Herman K. van Dijk Erasmus University Rotterdam
James J. Heckman
James J. Heckman University of Chicago
Håvard Rue
Håvard Rue King Abdullah University of Science and Technology
David B. Dunson
David B. Dunson Duke University
Gilles Celeux
Gilles Celeux French Institute for Research in Computer Science and Automation - INRIA
Stephen G. Walker
Stephen G. Walker The University of Texas at Austin

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