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| Name | Sudeep R Bapat |
| Qualification | Ph.D. (University of Connecticut, USA), Master of Science (Indian Institute of Technology, Bombay), Bachelor of Science (University of Delhi) |
| Contact No. | |
| sudeepb@iimidr.ac.in | |
| Curriculum Vitae | Download |
- Brief Profile
- Research Publications
Dr. Sudeep R. Bapat had his schooling and undergraduate education in New Delhi. He obtained his Bachelors degree from University of Delhi, a Masters in Science from the Indian Institute of Technology, Bombay and Ph.D. from the University of Connecticut, USA in 2017. His thesis mainly concentrates on the area of sequential methodologies and applications. Some of his PhD research includes estimation of parameters under a sequential sampling strategy, which is beneficial under many practical real world examples such as health studies, ecology and finance. Very recently, he worked as a visiting assistant professor at the department of statistics and applied probability at the University of California, Santa Barbara, USA, for three years.
His current research extends to the area of time series and censoring, where he has published several papers. Some of the notable works include, proposing a new correlation measure on a set of bivariate time series processes, introducing a new Lindley distribution, which has many interesting properties and proves advantageous over some of the existing ones and developing middle censoring models for a multinomial distribution.
As far as teaching goes, over the last three years at UC Santa Barbara, he has taught several courses in statistics at both the undergraduate and the graduate level. Some of which include Regression, Time Series, Design of Experiments, Probability and Inference etc. During summer of 2019, he also taught a course on “Introduction to analytics in R” at the Indian Statistical Institute, New Delhi at the certification program on “Business analytics, Data mining and Operations research”.
Dr. Bapat has presented his work at a lot of national and international conferences and seminars as an invited speaker. More recently, he organized and chaired a session at the International Indian Statistical Association conference held at IIT Bombay, in December of 2019.
- Mukhopadhyay, N. and Bapat, S. R. (2016). Multistage point estimation methodologies for a negative exponential location under a modified linex loss function: Illustrations with infant mortality and bone marrow data, Sequential Analysis, 35/2: 175-206. http://dx.doi.org/10.1080/07474946.2016.1165532 pdf
- Mukhopadhyay, N. and Bapat, S. R. (2016). Multistage estimation of the difference of locations of two negative exponential populations under a modified linex loss function: Real data illustrations from cancer studies and reliability analysis, Sequential Analysis, 35/3: 387-412. http://dx.doi.org/10.1080/07474946.2016.1206386 pdf
- Mukhopadhyay, N. and Bapat, S. R. (2017). Purely sequential bounded-risk point estimation of the negative binomial mean under various loss functions: one sample problem, Annals of the Institute of Statistical Mathematics, http://doi.org/10.1007/s10463-017-0620-2 pdf
- Mukhopadhyay, N. and Bapat, S. R. (2018). Purely sequential bounded-risk point estimation of the negative binomial means under various loss functions: multi-sample problems, Sequential Analysis, 36/4: 490-512. https://doi.org/10.1080/07474946.2017.1394708
- Bapat, S.R. (2018). Purely Sequential Fixed Accuracy Confidence Intervals for P(X < Y) under Bivariate Exponential Models, American Journal of Mathematical and Management Sciences, https://doi.org/10.1080/01966324.2018.1465867
- Bapat, S.R. (2018). On Purely Sequential Estimation of an Inverse Gaussian Mean, Metrika, https://doi.org/10.1007/s00184-018-0665-0
- Bapat, S. R. (2018). A New Correlation for Bivariate Time Series with a Higher Order of Integration, Communications in Statistics, Simulation and Computation, https://doi.org/10.1080/03610918.2018.1520875
- Mukhopadhyay, N. and Bapat, S. R. (2018). Renewed Looks at the Distribution of a Sum of Independent or Dependent Discrete Random Variables and Related Problems, Methodology and Computing in Applied Probability, https://doi.org/10.1007/s11009-018-9690-8
- Chaturvedi, A., Bapat, S. R. and Joshi, N. (2019). Sequential Minimum Risk Point Estimation of the Parameters of an Inverse Gaussian Distribution, American Journal of Mathematical and Management Sciences, https://doi.org/10.1080/01966324.2019.1570883
- Chaturvedi, A., Bapat, S. R. and Joshi, N. (2019). Second-Order Approximations for a Multivariate Analog of Behrens-Fisher Problem through Three-Stage Procedure, Communications in Statistics, Theory and Methods.
- Chaturvedi, A., Chattopadhyay, S., Bapat, S. R. and Joshi, N. (2019). Sequential Point Estimation Procedures for the Parameter of a Family of Distributions, Communications in Statistics, Simulation and Computation, https://doi.org/10.1080/03610918.2019.1612432
- Chaturvedi, A., Bapat, S. R. and Joshi, N. (2019). Multi-Stage Point Estimation of the Mean of an Inverse Gaussian Distribution, Sequential Analysis, 38/1: 1-25. https://doi.org/10.1080/01966324.2019.1570883
- Chaturvedi, A., Bapat, S. R. and Joshi, N. (2019). Multi-Stage Procedures for the Minimum Risk and Bounded Risk Point Estimation of the Location of Negative Exponential Distribution Under the Modified LINEX Loss Function, Sequential Analysis, 38/2: 135-162.
- Chaturvedi, A., Bapat, S. R. and Joshi, N. (2019). A k-Stage Procedure for Estimating the Mean Vector of a Multivariate Normal Population, Sequential Analysis, 38/3: 369-384.
- Chaturvedi, A., Bapat, S. R. and Joshi, N. (2020). Purely Sequential and k-Stage Procedures for Estimating the Mean of an Inverse Gaussian Distribution, Methodology and Computing in Applied Probability, https://doi.org/10.1007/s11009-019-09765-x
- Bapat, S. R. (2020). An Alternative Measure of Positive Correlation for Bivariate Time Series, Communications in Statistics, Simulation and Computation.
- Mazucheli, J., Bapat, S. R. and Menezes, A. F. B. (2020). A New One Parameter Unit-Lindley Distribution, Chilean Journal of Statistics, 11/1: 53-66.