Professor Renate Meyer

MSc/PhD (University of Aachen)

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I am an applied Bayesian statistician. After obtaining an MSc in Mathematics ( for which I was awarded the Springorum Denkmuenze) from the RWTH Aachen, Germany, I worked as a research scientist in the Department of Medical Statistics from 1988-1993 and did a PhD in Mathematical Statistics also at the RWTH Aachen. I then took up a lectureship in Statistics at the University of Auckland in 1994.

Research | Current

I am working on applied Bayesian inference and MCMC methods with interdisciplinary research collaborations and applications in astrophysics, econometrics, fisheries, marine ecology, medicine and engineering.

Contributions to the development of MCMC methodology have been published in Statistics and Computing, Computational Statistics and Data Analysis, and Computational Statistics.

My ongoing collaboration with astrophysicists Nelson Christensen, Carleton College, started in 1998 when we pioneered the MCMC approach to gravitational wave and cosmic microwave background radiation data analysis. This started a Marsden and NSF funded research program which led to world-wide collaborations and ground-breaking contributions to the development of MCMC-based data analysis strategies for LIGO with a series of publications in top-ranking Physics journals PRD and PRE. Some of these papers build the foundations of the MCMC techniques used to estimate the parameters of the two binary black hole mergers detected on September 14, 2015 by the Advanced LIGO interferometers and are cited in the parameter estimation paper that accompanied the famous detection paper by the LSC, published in Physical Review Letters on Feb. 11, 2016.

I am chair of the NZ Astrostatistics and General Relativity Group, which comprises statisticians and astrophysicists from the University of Auckland, Victoria, Canterbury and Otago. In 2019, we joined  the LISA (Laser Interferometric Space Antennna) Consortium that leads the European Space Agency mission of observing gravitational waves from space, see also the NZ Herald article.


In econometrics, my collaboration with Prof Jun Yu, Dept. of Economics, Singapore Management University, concentrates on a Bayesian approach to stochastic volatility models for financial time series. Our frequently cited papers have been published in JBES, The Econometrics Journal, and Econometric Reviews.

Collaborative research in Fisheries and Marine Science with A/Prof Russell Millar started in 1996 and established the Bayesian approach to fisheries stock assessment using state-space models. This method has become the standard for analysing biomass dynamics models and our papers published in Applied Statistics and CJFAS, one of the world’s top fisheries journal, have received close to 200 citations. Related collaborative research with A/Prof Mike Dowd, Dalhousie University, in Marine Ecology, is in Bayesian statistical data assimilation using state space models where we have developed particle filtering methods for high-dimensional spatio-temporal ecosystems. Publications are in Ecological Modelling and Envirometrics.

A further main focus of my research is in Medical Statistics with recent papers on Bayesian approaches to analysing stratified survival data, time-dependent frailties and copula models for multivariate survival data. Papers have been published in Statistics in Medicine, CSDA, and JRSSA. Last not least, Bayesian models for stratified survival data had significant applications in Engineering, where we developed new hierarchical models for pipe failure times of water distribution systems.


  • Bayesian statistics
  • Markov chain Monte Carlo methods
  • Survival analysis, multiple events, non-and semi-parametric models
  • copulas
  • Astrophysics, gravitational waves, cosmic microwave background radiation
  • State-space modelling
  • Fisheries stock assessment
  • Econometrics, financial time series, stochastic volatility models
  • Longitudinal data analysis, dynamic and semiparametric models

Past research interests

  • multivariate analysis
  • matrix methods for statistics
  • algorithms in multidimensional scaling
  • correspondence analysis

Teaching | Current

  • 2010, Guest Lecture at the Karlsruhe Institute of Technology, Germany,    Applied Bayesian Inference, Wintersemester 2010/11
  • 2011   STATS731, Bayesian Inference (graduate)
  • 2012   STATS731 Bayesian Inference (graduate)
  • 2013   STATS731 Bayesian Inference (graduate)
  • 2013   STATS763 Advanced Applied Statistics (PhD)
  • 2014   STATS732Topics in Statistical Inference (graduate)
  • 2014    STATS763 Advanced Theoretical Statistics (PhD),
  • 2014    STATS731 Bayesian Inference (graduate)
  • 2015   STATS732 Topics in Statistical Inference (graduate),
  • 2015   STATS763 Advanced Theoretical Statistics (PhD)
  • 2015   STATS331 Introduction to Bayesian Statistics (undergraduate) ,
  • 2015   STATS731 Bayesian Inference (graduate)
  • 2016   STATS731 Bayesian Inference (graduate)
  • 2016   STATS766  Multivariate Statistics (graduate)
  • 2017   STATS731 Bayesian Inference (graduate)
  • 2017   STATS766 Multivariate Statistics (graduate)
  • 2017   Short Course: Applied Bayesian Inference, Otto-von-Guericke University, Magdeburg,
  • 2018   STATS731 Bayesian Inference (graduate)
  • 2019   STATS731 Bayesian Inference (graduate)


Postgraduate supervision

Postdoctoral Research Fellows

 PhD students

  • Alexander Meier, Thesis topic: "A Matrix Gamma Process with applications to Bayesian analysis of multivariate time series" (2016- 2019)
  • Matthew Edwards Thesis topic:"Bayesian modelling of stellar core collapse gravitational wave signals and detector noise" (2013-2017)
  • Jonathan Briggs , Thesis topic: "Bayesian state space modelling for data assimilation in phytoplankton ecosystems" (2007-2011)
  • Asad Ali , Thesis topic: "Monte Carlo Methods for LISA Data Analysis" (2007-2011)
  • Christian Roever, Thesis topic: "Bayesian Inference on Astrophysical Binary Inspirals Based on Gravitational-Wave Measurements" (2004-2007)
  • Richard Umstaetter, Thesis topic: "Bayesian Strategies for Gravitational Radiation Data Analysis" (2003-2006)
  • Andreas Berg, Thesis topic: "Bayesian Analysis of Stochastic Volatility Models for Financial Time Series" (2000-2004)
  • Bo Cai, Thesis topic: "Adaptive Sampling Schemes and Bayesian Semiparametric Survival Analysis" (1999-2003)


James Cook Fellowship, Royal Society of New Zealand, (2019-2021)

Borchers Plakette, Postdoctoral Award of the University of Aachen for promotion with "summa cum laude" (1994)

Springorum Denkmunze, Postgraduate Award of the University of Aachen for MSc with distinction (1988)


Graduate Officer for PhD

Group Leader of NZ Astrostatistics and General Relativity Group within LISA Consortium

Member of Research Committee, Department of Statistics

Areas of expertise

Bayesian inference, MCMC, copulas, state-space models, survival analysis, gravitational radiation data analysis, stochastic volatility models

Selected publications and creative works (Research Outputs)

  • Gallardo, D. I., Romeo, J. R., & Meyer, R. (2017). A simplified estimation procedure based on the EM algorithm for the power series cure rate model. Communications in Statistics - Simulation and Computation, 46 (8), 6342-6359. 10.1080/03610918.2016.1202276
  • Meyer, R., & Christensen, N. (2016). Gravitational waves: A statistical autopsy of a black hole merger. Significance, 13 (2), 20-25. 10.1111/j.1740-9713.2016.00896.x
  • Meyer, R., & Romeo, J. S. (2015). Bayesian semiparametric analysis of recurrent failure time data using copulas. Biometrical Journal, 57 (6), 982-1001. 10.1002/bimj.201400125
  • Manda, S., Masenyetse, L., Cai, B., & Meyer, R. (2015). Mapping HIV prevalence using population and antenatal sentinel-based HIV surveys: A multi-stage approach. Population Health Metrics, 13 (1).10.1186/s12963-015-0055-z
  • Edwards, M., Meyer, R., & Christensen, N. L. (2015). Bayesian semiparametric power spectral density estimation with applications in gravitational wave data analysis. Physical Review D - Particles, Fields, Gravitation, and Cosmology, 92 (6).10.1103/PhysRevD.92.064011
    Other University of Auckland co-authors: Matt Edwards
  • Meyer, R., & Romeo, J. S. (2015). Bayesian semiparametric analysis of recurrent failure time data using copulas. Biometrical Journal10.1002/bimj.201400125
  • Romeo, J. S., Meyer, R., & Reyes-Lopez, F. E. (2014). Hierarchical failure time regression using mixtures for classification of the immune response of Atlantic salmon. Journal of Agricultural, Biological, and Environmental Statistics, 19 (4), 503-523. 10.1007/s13253-014-0188-8
  • Kauermann, G., & Meyer, R. (2014). Penalized marginal likelihood estimation of finite mixtures of Archimedean copulas. Computational Statistics, 29 (1-2), 283-306. 10.1007/s00180-013-0454-1


Contact details

Primary office location

Level 3, Room 365
New Zealand

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