Estimation of Multiple Poisson Means: A Compromise Between Maximum Likelihood and Empirical Bayes Estimators.
M. Ghosh, F. Zamudio (1987)
Metrika
Similarity:
Displaying similar documents to “The Asymptotic Properties of Maximum Likelihood Estimators for Marked Poisson Processes with a Cyclic Intensity Measure.”
Estimation of Multiple Poisson Means: A Compromise Between Maximum Likelihood and Empirical Bayes Estimators.
Asymptotic Efficiency of Statistical Estimators: Concepts and Higher Order Asymptotic Efficienc - Akahira, M.;Takeuchi, K.
E. Siebert (1983)
Metrika
Similarity:
Quick Consistency of Quasi-Maximum Likelihood Estimators in a Multivariate Poisson Process.
Zbigniew Szkutnik (1996)
Metrika
Similarity:
Limited Deviation Estimators of the Poisson Parameter.
S.H. Shapiro (1980)
Metrika
Similarity:
Quasi-Maximum Likelihood Estimation of Parameters in a Multivariate Poisson Process.
Zbigniew Szkutnik (1996)
Metrika
Similarity:
Some methods of estimation for a trivariate Poisson distribution
S Loukas (1991)
Applicationes Mathematicae
Similarity:
Estimation of the Parameters of the Weibull Distribution for the Censored Samples.
Waltraud Kahle (1996)
Metrika
Similarity:
Asymptotic evaluation of the Poisson measures for tubes around jump curves
Xavier Bardina, Carles Rovira, Samy Tindel (2002)
Applicationes Mathematicae
Similarity:
We find the asymptotic behavior of P(||X-ϕ|| ≤ ε) when X is the solution of a linear stochastic differential equation driven by a Poisson process and ϕ the solution of a linear differential equation driven by a pure jump function.
Estimation of interarrival time distribution from short time windows
Zbyněk Pawlas (2011)
Acta Universitatis Carolinae. Mathematica et Physica
Similarity:
Asymptotic normality in mixture models
Sara Van De Geer (2010)
ESAIM: Probability and Statistics
Similarity:
We study the estimation of a linear integral functional of a distribution F, using i.i.d. observations which density is a mixture of a family of densities k(.,y) under F. We examine the asymptotic distribution of the estimator obtained by plugging the non parametric maximum likelihood estimator (NPMLE) of F in the functional. A problem here is that usually, the NPMLE does not dominate F. Our main aim here is to show that this can be overcome by considering a convex combination...