Displaying similar documents to “Extensions of the Frisch-Waugh-Lovell Theorem”

A note on Steinhorn's omitting types theorem

Akito Tsuboi (2009)

Colloquium Mathematicae

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Let p(x) be a nonprincipal type. We give a sufficient condition for a model M to have a proper elementary extension omitting p(x). As a corollary, we obtain a generalization of Steinhorn's omitting types theorem to the supersimple case.

Identification of parametric models with a priori knowledge of process properties

Krzysztof B. Janiszowski, Paweł Wnuk (2016)

International Journal of Applied Mathematics and Computer Science

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An approach to estimation of a parametric discrete-time model of a process in the case of some a priori knowledge of the investigated process properties is presented. The knowledge of plant properties is introduced in the form of linear bounds, which can be determined for the coefficient vector of the parametric model studied. The approach yields special biased estimation of model coefficients that preserves demanded properties. A formula for estimation of the model coefficients is derived...

Generalized CreditRisk+ model and applications

Jakub Szotek (2015)

Annales Universitatis Paedagogicae Cracoviensis. Studia Mathematica

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In the paper we give a mathematical overview of the CreditRisk+ model as a tool used for calculating credit risk in a portfolio of debts and suggest some other applications of the same method of analysis.

Some Parameter Estimation Issues in Functional-Structural Plant Modelling

P.-H. Cournède, V. Letort, A. Mathieu, M. Z. Kang, S. Lemaire, S. Trevezas, F. Houllier, P. de Reffye (2011)

Mathematical Modelling of Natural Phenomena

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The development of functional-structural plant models has opened interesting perspectives for a better understanding of plant growth as well as for potential applications in breeding or decision aid in farm management. Parameterization of such models is however a difficult issue due to the complexity of the involved biological processes and the interactions between these processes. The estimation of parameters from experimental data by...

Variance function estimation via model selection

Teresa Ledwina, Jan Mielniczuk (2010)

Applicationes Mathematicae

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The problem of estimating an unknown variance function in a random design Gaussian heteroscedastic regression model is considered. Both the regression function and the logarithm of the variance function are modelled by piecewise polynomials. A finite collection of such parametric models based on a family of partitions of support of an explanatory variable is studied. Penalized model selection criteria as well as post-model-selection estimates are introduced based on Maximum Likelihood...