Let there be a set F of mathematical models L m(x) for the description of the depend once of a real random variable y(x) on , where m is an element of a subscript set M which may be continuous. Let . We suppose that the models are linearly dependent on the parameters. The observation…
Mathematische Operationsforschung Statistik Template
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About the Mathematische Operationsforschung Statistik format
Mathematische Operationsforschung Statistik is a peer-reviewed journal published by Taylor & Francis, covering Advanced Statistical Methods and Models, Advanced Queuing Theory Analysis, Advanced Optimization Algorithms Research.
| 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." Mathematische Operationsforschung Statistik 12 (3): 45–58.
Formats any DOI in Mathematische Operationsforschung Statistik style. No sign-up. |
| Publishes research in | Advanced Statistical Methods and Models Advanced Queuing Theory Analysis Advanced Optimization Algorithms Research Optimization and Variational Analysis Physics and Engineering Research Articles |
| ISSN | 0047-6277 |
| h-index | 25 |
| i10-index | 58 |
| Total citations | 2,327 |
| 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. |
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Most-cited papers in Mathematische Operationsforschung Statistik
Abstract In the paper an authoregressive model is introduced and investigated, the parameters of which are random variables. The necessary and sufficient conditions for stationarity are derived. It is shown that the covariance function of a stationary autorgressive series with random parameters satisifies the same Yule-Walker equations as in the usual autoregressive model with fixed…
For a linear modely = Xβ + ε with additional information about β given by β′ H β ≦ σ 2 a minimax linear estimation for a parameter γ =Cβ is derived for a quadratic loss. Therefore a matrix optimization problem is solved which can be use for several other minimax problems.
Abstract In this paper multipurpose designs are considered. Therefore the criterion is used to choose a design, where Mm(ξ) are the information matrices of a design ξ for several models. With suitable chosen matrices Amj, a S-optimal design is good for the estimation of parameters in several models at the same time or for the…
Abstract For a linear model with restrictions of the normed linear parameter to a generalized ellipsoid it is proven, that the Kuks-Olman estimator is minimax linear with respect to the matrik risk. From this follow optimal ridge and shrunken estimators.