{"id":56,"date":"2021-02-23T10:44:58","date_gmt":"2021-02-23T15:44:58","guid":{"rendered":"http:\/\/pages.charlotte.edu\/jpbishwa\/?page_id=56"},"modified":"2021-09-09T14:30:15","modified_gmt":"2021-09-09T18:30:15","slug":"publications","status":"publish","type":"page","link":"https:\/\/pages.charlotte.edu\/jpbishwa\/publications\/","title":{"rendered":"Publications"},"content":{"rendered":"\n

Google Scholar Citations<\/a>
Books   Book Flyer<\/a> <\/h3>\n\n\n\n
\"\"<\/a><\/figure><\/div>\n\n\n\n

1. Parameter Estimation in Stochastic Differential Equations<\/h2>\n\n\n\n

Softcover ISBN: 978-3-540-74447-4  eBook ISBN: 978-3-540-74448-1
DOI: 10.1007\/978-3-540-74448-1 Lecture Notes in Mathematics Series<\/a>
Volume 1923 (2008)<\/a>   Springer-Verlag<\/a>   Zentralblatt Math Review<\/a>
Core Titles in: Probability Theory and Stochastic Processes;<\/a> Quantitative Finance;<\/a> Statistical Theory and Methods;<\/a> Analysis;<\/a> Numerical Analysis;<\/a> Computational Science and Engineering;<\/a> Game Theory, Economics, Social and Behavioral Sciences.<\/a>  WorldCatalog<\/a> Amazon.com<\/a> Amazon.co.uk<\/a> Amazon.fr<\/a> Amazon.de<\/a>  
Book Performance Reports: 2012;<\/a>  2013;<\/a>  2014;<\/a>  2016;<\/a>  
2017;  (26,613 chapter downloads till 2017)<\/a><\/p>\n\n\n\n

2. Parameter Estimation in Stochastic Volatility Models<\/h2>\n\n\n\n

(To be published by Springer Nature Switzerland AG
May 2021 [600 pages], Under Contract.)<\/p>\n\n\n\n

PAPERS<\/h3>\n\n\n\n

44. A new algorithm for approximate maximum likelihood estimation in sub-fractional Chan-Karloyi-Longstaff-Sanders model,\u00a0Asian Journal of Probability and Statistics<\/a>\u00a013(3) (2021), 62-88.<\/p>\n\n\n\n

43. Bernstein-von Mises theorem and small noise asymptotics of Bayes estimators for parabolic stochastic partial differential equations, Theory of Stochastic Processes<\/a> 23 (1) (2018), 6-17.<\/p>\n\n\n\n

42. Sequential maximum likelihood estimation in nonlinear non-Markov diffusion type processes, Dynamic Systems and Applications<\/a> 27 (1) (2018), 107-124.<\/p>\n\n\n\n

41. Robust estimation in Gompertz diffusion model of tumor growth, Open Access Biostatistics and Bioinformatics<\/a>  1 (5) (2018), 1-5.<\/p>\n\n\n\n

40. Conditional least squares estimation for discretely sampled nonergodic diffusions, Asian Research Journal of Mathematics<\/a> 7 (4) (2017), 1-18.<\/p>\n\n\n\n

39. Maximum likelihood estimation in nonlinear fractional stochastic volatility model, Asian Research Journal of Mathematics<\/a> 6 (2) (2017), 1-11.<\/p>\n\n\n\n

38. Valuation of real options under persistent shocks, Journal of Statistics and Management Systems<\/a> 20 (5) (2017), 801-815.<\/p>\n\n\n\n

37. Hypothesis testing for fractional stochastic partial differential equations (fSPDEs) with applications to neurophysiology and finance, Asian Research Journal of Mathematics<\/a> 4 (1) (2017), 1-24.<\/p>\n\n\n\n

36. Nonparametric estimation of Heath-Jarrow-Morton term structure models driven by fractional Levy processes using local time, (2016).<\/p>\n\n\n\n

35. Method of moments estimation in Gamma-Ornstein-Uhlenbeck stochastic volatility model, (2015).<\/p>\n\n\n\n

34. Parameter estimation for SPDEs with non-commuting operators based on discrete sampling, (2014).<\/p>\n\n\n\n

33. Martingale estimation function for Poissonly observed stochastic partial differential equations, (2013).<\/p>\n\n\n\n

32. Sequential maximum likelihood estimation for reflected Ornstein-Uhlenbeck processes (with Chihoon Lee and Myung Lee), Journal of Statistical Planning and Inference<\/a> 142 (5) (2012), 1234-1242.<\/p>\n\n\n\n

31. Stochastic moment problem and hedging of generalized Black-Scholes options, Applied Numerical Mathematics<\/a> 61 (12) (2011), 1271-1280.<\/p>\n\n\n\n

30. Minimum contrast estimation in fractional Ornstein-Uhlenbeck process: continuous and discrete sampling, Fractional Calculus and Applied Analysis<\/a> 14 (3) (2011), 375-410.<\/p>\n\n\n\n

29. Berry-Esseen inequalities for discretely observed Ornstein-Uhlenbeck-Gamma process, Markov Processes and Related Fields<\/a> 17 (1) (2011), 119-150.<\/p>\n\n\n\n

28. Maximum quasi-likelihood estimation in fractional Levy stochastic volatility model, Journal of Mathematical Finance<\/a> 1 (3) (2011), 58-62.<\/p>\n\n\n\n

27. Sufficiency and Rao-Blackwellization of Vasicek model, Theory of Stochastic Processes<\/a> 17 (33) (1) (2011), 12-15.<\/p>\n\n\n\n

26. Financial extremes: a short review, Advances and Applications in Statistics<\/a> 25 (1) (2011), 1-14.<\/p>\n\n\n\n

25. Some new estimators of integrated volatility, Open Journal of Statistics<\/a> 1 (2) (2011), 74-80.<\/p>\n\n\n\n

24. Sieve estimator for fractional stochastic partial differential equations, Annals of Constantin Brancusi<\/a> 5 (1) (2011), 9-18.<\/p>\n\n\n\n

23. Estimation in interacting diffusions: continuous and discrete sampling, Applied Mathematics<\/a> 2 (9) (2011), 1154-1158.<\/p>\n\n\n\n

22. Milstein approximation of posterior density for diffusions, International Journal of Pure and Applied Mathematics<\/a> 68 (4) (2011), 403-414<\/p>\n\n\n\n

21. Maximum likelihood estimation in Skrorohod stochastic differential equations, Proceedings of the American Mathematical Society<\/a> 138 (4) (2010), 1471-1478.<\/p>\n\n\n\n

20. Uniform rate of weak convergence of the minimum contrast estimator in the Ornstein-Uhlenbeck process, Methodology and Computing in Applied Probability<\/a> 12 (3) (2010), 323-334.<\/p>\n\n\n\n

19. Conditional least squares estimation in diffusion processes based on Poisson sampling, Journal of Applied Probability and Statistics<\/a> 5 (2) (2010), 169-180.<\/p>\n\n\n\n

18. Sequential Monte Carlo methods for stochastic volatility models: a review, Journal of Interdisciplinary Mathematics<\/a> 13 (6) (2010), 619-635.<\/p>\n\n\n\n

17. M-estimation for discretely sampled diffusions, Theory of Stochastic Processes<\/a> 15 (31) (2) (2009), 62-83.<\/p>\n\n\n\n

16. Berry-Esseen inequalities for discretely observed diffusions, Monte Carlo Methods and Applications<\/a> 15 (3) (2009), 229-239<\/p>\n\n\n\n

15. Large deviations in testing fractional Ornstein Uhlenbeck models, Statistics & Probability Letters<\/a> 78 (8) (2008), 953-962.<\/p>\n\n\n\n

14. Large deviations and Berry-Esseen inequalities for estimators in nonlinear nonhomogeneous diffusions, RevStat – Statistical Journal<\/a> 5 (3) (2007), 249-267.<\/p>\n\n\n\n

13. A new estimating function for discretely sampled diffusions, Random Operators and Stochastic Equations<\/a> 15 (1) (2007), 65-88.<\/p>\n\n\n\n

12. Sequential maximum likelihood estimation in semimartingales, Journal of Statistics and Applications<\/a> 1 (2-4) (2006), 143-153.<\/p>\n\n\n\n

11. Rates of weak convergence of approximate minimum contrast estimators for the discretely observed Ornstein-Uhlenbeck process, Statistics & Probability Letters<\/a> 76 (13) (2006), 1397-1409.<\/p>\n\n\n\n

10. Maximum likelihood estimation in partially observed stochastic differential system driven by a fractional Brownian motion, Stochastic Analysis and Applications<\/a> 21 (5) (2003), 995-1007.<\/p>\n\n\n\n

9. The Bernstein-von Mises theorem and spectral asymptotics of Bayes estimators for parabolic SPDEs,  Journal of the Australian Mathematical Society<\/a> 72 (2) (2002), 287-298.<\/p>\n\n\n\n

8. Rates of convergence of approximate maximum likelihood estimators in the Ornstein-Uhlenbeck process, Computers & Mathematics with Applications<\/a> 42 (1-2) (2001), 23-38 (with Arup Bose).<\/p>\n\n\n\n

7. Accuracy of normal approximation for the maximum likelihood and the Bayes estimators in the Ornstein-Uhlenbeck process using random normings, Statistics & Probability Letters<\/a> 52 (4) (2001), 427-439.<\/p>\n\n\n\n

6. Rates of convergence of the posterior distributions and the Bayes estimators in the Ornstein-Uhlenbeck process, Random Operators and Stochastic Equations<\/a> 8 (1) (2000), 51-70.<\/p>\n\n\n\n

5. Sharp Berry-Esseen bound for the maximum likelihood estimator in the Ornstein- Uhlenbeck process, Sankhy\u0101 Series A 62 (1), (2000), 1-10.<\/p>\n\n\n\n

4. Large deviations inequalities for the maximum likelihood estimator and the Bayes estimators in nonlinear stochastic differential equations, Statistics & Probability Letters<\/a> 43 (2) (1999), 207-215.<\/p>\n\n\n\n

3. Bayes and sequential estimation in Hilbert space valued stochastic differential equations, Journal of the Korean Statistical Society<\/a> 28 (1) (1999), 96-108.<\/p>\n\n\n\n

2. Speed of convergence of the maximum likelihood estimator in the Ornstein-Uhlenbeck process, Calcutta Statistical Association Bulletin<\/a> 45 (1995), 245-251(with Arup Bose).<\/p>\n\n\n\n

1. Approximate maximum likelihood estimation for diffusion processes from discrete observations, Stochastics<\/a> 52 (1995), 1-13 (with M. N. Mishra).<\/p>\n\n\n\n

Technical Report<\/h3>\n\n\n\n

1. A note on inference in a bivariate normal distribution model (with Edsel Pena) SAMSI Technical Report #2009-3<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

Google Scholar CitationsBooks   Book Flyer  1. Parameter Estimation in Stochastic Differential Equations Softcover ISBN: 978-3-540-74447-4  eBook ISBN: 978-3-540-74448-1DOI: 10.1007\/978-3-540-74448-1 Lecture Notes in Mathematics SeriesVolume 1923 (2008)   Springer-Verlag   Zentralblatt Math ReviewCore Titles in: Probability Theory and Stochastic Processes; Quantitative Finance; Statistical Theory and Methods; Analysis; Numerical Analysis; Computational Science and Engineering; Game Theory, Economics, Social and Behavioral Sciences.  WorldCatalog Amazon.com Amazon.co.uk Amazon.fr Amazon.de  Book Performance Reports: 2012;  2013;  2014;  2016;  2017;  (26,613 chapter downloads till 2017) 2. Parameter […]<\/p>\n","protected":false},"author":13,"featured_media":0,"parent":0,"menu_order":30,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-56","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/pages.charlotte.edu\/jpbishwa\/wp-json\/wp\/v2\/pages\/56","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/pages.charlotte.edu\/jpbishwa\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/pages.charlotte.edu\/jpbishwa\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/pages.charlotte.edu\/jpbishwa\/wp-json\/wp\/v2\/users\/13"}],"replies":[{"embeddable":true,"href":"https:\/\/pages.charlotte.edu\/jpbishwa\/wp-json\/wp\/v2\/comments?post=56"}],"version-history":[{"count":33,"href":"https:\/\/pages.charlotte.edu\/jpbishwa\/wp-json\/wp\/v2\/pages\/56\/revisions"}],"predecessor-version":[{"id":284,"href":"https:\/\/pages.charlotte.edu\/jpbishwa\/wp-json\/wp\/v2\/pages\/56\/revisions\/284"}],"wp:attachment":[{"href":"https:\/\/pages.charlotte.edu\/jpbishwa\/wp-json\/wp\/v2\/media?parent=56"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}