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D-Index
37
Citations
8762
World Ranking
2434
National Ranking
30

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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