Click to increase image sizeClick to decrease image size 2Paper given at the 3rd International Summer School on Problems of Model Choice and Parameter Estimation in Regression Analysis, Mühlhausen (G.D.R.), May 1977 Notes 2Paper given at the 3rd International Summer School on Problems of Model Choice and Parameter Estimation in Regression Analysis, Mühlhausen (G.D.R.), May…
Series Statistics Template
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About the Series Statistics format
Series Statistics is a peer-reviewed journal published by Taylor & Francis, covering Advanced Statistical Methods and Models, Statistical Methods and Inference, Bayesian Methods and Mixture Models.
| Publisher | Taylor & Francis |
|---|---|
| Reference style | Author–year (Chicago, T&F) Author–year — (Smith, 2023) in the text Smith, Ada, Ben Jones, and Cara Lee. 2023. "A Representative Article Title." Series Statistics 12 (3): 45–58.
Formats any DOI in Series Statistics style. No sign-up. |
| Publishes research in | Advanced Statistical Methods and Models Statistical Methods and Inference Bayesian Methods and Mixture Models Optimal Experimental Design Methods Statistical Distribution Estimation and Applications |
| ISSN | 0323-3944 |
| h-index | 27 |
| i10-index | 72 |
| Total citations | 2,778 |
| Top institutions publishing here | TU Bergakademie Freiberg |
| Journal website | www.tandfonline.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Papers published in Series Statistics per year
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Most-cited papers in Series Statistics
For the reduced second moment measure of stationary point processes several estimators are presented. In order to show the most general conditions under which the estimators are applicable, some properties of non-stationary point processes are introduced making it possible to reduce the second moment measure as in the stationary case. For the stationary Poisson process…
(1980). Maximum likelihood estimation of variance components. Series Statistics: Vol. 11, No. 4, pp. 545-561.
Let (Xi,Yi), (X2,Y2),… be independent, identically distributed pairs of r.v.'s such that .Let r0(x)≡0 and where αn=n-α(0<α<1) and K(.) is a suitable ensity function. Theorems are proved stating that r n (x) is a good estimation of r(x).
Asymptotic normality and asymptotic efficiency of the least squares estimator of a parameter in the non-linear drift coefficient of the Ito stochastic differential equation are obtained under some regularity conditions. Some examples are presented.