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Robert Durrant

Harold Henderson

Hans Hockey

Lyn Hunt

Chaitanya Joshi

Yoonsuh Jung

Ray Littler

Steven Miller

Tania Robinson

Department of Statistics
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Chaitanya Joshi (Dr)

BSc Mumbai; MSc IIT-K; PhDTCD

Details

  • Room No: G.3.29
  • Telephone: +64 7 838 4019
  • Extension: 4019
  • Facsimile: +64 7 838 4155
  • Email: cjoshi@waikato.ac.nz

Further information can be found at my personal web site: http://www.cms.waikato.ac.nz/~cjoshi.

image of staff member

Personal Description
After completing my MSc(Statistics) from the Indian Institute of Technology Kanpur in 2003, I decided to work as a Statistician in Industry. During my stints with various organisations, I ended up working in the area of Market Research and Econometric Modeling for about a year and on Clinical trials for three years. Finally, however, I was convinced that it wasn't for me (I had had enough!) I decided to go back to academic life and joined the PhD program at the Department of Statistics at Trinity College, Dublin. After completing my PhD, I also did a (short) post-doc at Trinity before joining the Department of Statistics, University of Waikato as a Lecturer in July 2011.

Research Interests
My research interests primarily include

Bayesian modeling of complex systems
Developing computationally efficient (non-MCMC based) methods for inference and
Randomised-Quasi Monte Carlo integration

The second research interest often stems from the first one. For example, my PhD research was concerned with Bayesian modeling of the dynamic force exerted by vehicles on the road surface - this was essentially a problem of Bayesian inference on the parameters of a system of second order differential equations. However, MCMC based methods implemented on complex systems such as this can be computationally very expensive to the extent that they limit the quality of the inference. Therefore, as part of that research, I developed a new approach to implement computationally efficient Bayesian Inference on Stochastic Differential Equation (SDE) models. Presently, I am working on writing a proposal to extend this approach to a more general class of models and also explore the ways to improve its efficiency. I am also interested in the areas of Low-discrepancy sequences and Randomised Quasi-Monte Carlo Integration (RQMC). Currently I am working (along with Prof John Haslett, TCD) on developing a computationally efficient method for estimating the variance of the RQMC estimate. This work is leading to some interesting insights into the nature of RQMC sequences.

Publications - 2013

Conference Proceedings

    Joshi, C., Brown, P.T., and Laughlin, D.C. (2013) Inferential tests and modelling of functional trail convergence along environmental gradients Proceedings of the World Statistics Congress 2013 (in print)

Publications - 2012

Conference Papers

    Joshi, C., Laughlin, D.C., Van Bodegom, P.M., Bastow, Z.A., and Fule, P.Z. (2012) Modeling trait based ecological community assembly Proceedings of the 27th International Workshop on Statistical Modelling, Volume I, pages 177-178. Publisher: Tribune EU

Conference Presentations

    Joshi, C. (2012) On Computationally Efficient Estimation of the Variance of the Randomised Quasi Monte Carlo Estimate 10th International Conference on Monte Carlo and Quasi Monte Carlo Methods in Scientific Computing (MCQMC 2012), Sydney, Australia

Non-Refereed Journal Articles

    Laughlin, D.C., Joshi, C., Van Bodegom, P.M., Bastow, Z.A. and Fule, P.Z. (2012) A predictive model of community assembly that incorporates intraspecific trail variation. Blackwell Publishing Ltd; pages 1291-1299 (November 2012)

Publications - 2011

Articles in Refereed Journals

    Haslett, J., Joshi, C. and Geveli, R.V. (2011) On efficient estimation of the variance of the Randomised Quasi Monte Carlo estimate TCD-Statistifcs-11-08. trinity College, Dublin, p.1.13.

  2007 FCMS. The University of Waikato - Te Whare Wananga o Waikato