By Soumendra Lahiri, Anton Schick, Ashis SenGupta, T.N. Sriram
This quantity highlights Prof. Hira Koul’s achievements in lots of components of information, together with Asymptotic concept of statistical inference, Robustness, Weighted empirical methods and their purposes, Survival research, Nonlinear time sequence and Econometrics, between others. Chapters are all unique papers that discover the frontiers of those parts and may support researchers and graduate scholars operating in statistics, Econometrics and comparable components. Prof. Hira Koul was once the 1st Ph.D. scholar of Prof. Peter Bickel. His distinct profession in facts comprises the receipt of many prestigious awards, together with the Senior Humbolt award (1995), and devoted provider to the career via editorial paintings for journals and during management roles in expert societies, significantly because the prior president of the foreign Indian Statistical organization. Prof. Hira Koul has graduated with regards to 30 Ph.D. scholars, and made numerous seminal contributions in approximately one hundred twenty five leading edge study papers. The lengthy checklist of his individual collaborators is represented through the members to this volume.
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Additional resources for Contemporary Developments in Statistical Theory: A Festschrift for Hira Lal Koul
4 Non-Ergodic Martingale Estimating Functions As discussed in Sect. , n) is used to get asymptotic normal distributions of the various estimators obtained from MEFs. On the other hand, for nonergodic type processes, limit distributions of standard estimators are mixed-normal when a nonrandom norm is used. Instead, a random norm is required to get normal limit distributions for nonergodic processes. Asymptotics of various statistics normalized by random norms in a broad context have recently been discussed by Pena et al.
R. N. Shanbhag (eds) Handbook of statistics, Vol. 19. North Holland, pp 55–77 Basawa IV, Koul HL (1988) Large-sample statistics based on quadratic dispersion. Inter Statist Review 56:199–219 Basawa IV, Prakasa Rao BLS (1980a) Statistical inference for stochastic processes. Academic Press, London Basawa IV, Prakasa Rao BLS (1980b) Asymptotic inference for stochastic processes. Stochastic Proc their Appl 10:221–254 Basawa IV, Scott DJ (1983) Asymptotic optimal inference for non-ergodic models. Springer, New York Basawa IV, Feigin PD, Heyde CC (1976) Asymptotic properties of maximum likelihood estimators for stochastic processes.
Fn to estimate the distribution function of εi , and provides consistency and distributional F. edu S. Lahiri et al. 1007/978-3-319-02651-0_3, © Springer International Publishing Switzerland 2014 29 30 F. Cheng convergence results for a class of statistics based on Fn . f.. Cheng (2008b) develops the asymptotic distribution of the innovation density estimator at a fixed point and globally. Cheng and Wen (2011) obtain the strong consistency of the innovation density estimator under L1 -norm. Cheng, Sun, and Wen (2011) develop the asymptotic normality of the Bickel-Rosenblatt test statistic and show the strong consistency of the estimator for the true density in L2 -norm.