CSNano Seminar Series 12: Dr. Budhi Arta Surya

The event is open to the public. All are welcome to attend.

Scientific Talk

Dr. Budhi Arta Surya
School of Mathematics and Statistics
Victoria University of Wellington
Gate 7 Kelburn Parade
Wellington 6140, New Zealand
Phone: +64 4 463 5669
Mobile: +64 21 1568 674


Date : 11 January 2017 (Wednesday)
Time : 10.30 am – 12.00 am
Venue : Meeting Room, N31, Centre for Sustainable Nanomaterials (CSNano), Ibnu Sina Institute for Scientific and Industrial Research, Universiti Teknologi Malaysia, Skudai, Johor Bahru 
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Budhi gained his PhD in Applied Mathematics from Utrecht University, the Netherlands. Soon after that, he joined Bank of America Corp. as a Quantitative Financial Analyst based in Singapore. His research has been mainly on Applied Probability and Stochastic Modeling motivated by problems in actuarial science, credit risk and financial economics. He was a visiting scholar to Columbia University in NYC, LSE, Bath, Goethe University in Frankfurt, Osaka, NTU and NCTU in Taiwan, CUHK, Monash and ANU at which he was invited to give a talk and do his research. He secured some research grants from DAAD, Victoria University PBRF Research Grants and recently from CSIRO Australia for a joint PhD research grants with B-School Monash University and CSIRO. Budhi joined Victoria University of Wellington in February 2015.

Presentation Abstract

“Generalized Phase-Type Distribution Under Markov Mixtures Process”

Dr. Budhi Arta Surya

Phase-type model has been an important probabilistic tool in the analysis of complex stochastic system evolution. It was introduced by Neuts in 1975. The model describes the lifetime distribution of a continuous-time finite-state absorbing Markov chains. It has found many applications in wide range of areas such as e.g. in actuarial science, credit risk, financial economics, queuing theory, reliability theory, telecommunications, etc. It was brought to survival analysis by Aalen in 1995. However, the model has lacks of ability in modeling heterogeneity and inclusion of past information which is due to the Markov property of the underlying process that forms the model. We attempt to generalize the model by replacing the underlying by Markov mixtures process. Markov mixtures process was used to model jobs mobility by Blumen et al. in 1955. It was known as the mover-stayer model. The model describes low productivity workers tendency to move out of their jobs occupancy, while those with high-productivity tend to avoid job turnover. The model was extended by Frydman (2005) to a mixtures of finite-state Markov chains moving at different speeds on the same state space. In general the mixtures process does not have Markov property. We revisit the Markov mixtures model for absorbing Markov chains moving at different speeds on the same finite-state space, and propose generalization of the phase-type model with multi absorbing states. The later allows us to cope with competing risks in survival analysis. The new distribution has two main appealing features: it has the ability to model heterogeneity and allows the inclusion of past information of the underlying process, and it comes in a closed form. Built upon the new distribution, we propose conditional forward (cause-specific) intensity which can be used to determine rate of occurrence of future events (caused by certain type) based on available past information. Numerical study suggests that the new distribution and its forward intensity offer significant improvements over the existing model