Results 261 to 270 of about 26,321,511 (299)
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American Journal of Orthodontics and Dentofacial Orthopedics, 2023
Tomasz Burzykowski +3 more
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Tomasz Burzykowski +3 more
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A nonparametric general linear model
Computers and Biomedical Research, 1972Abstract A matrix formulation of the Kruskal-Wallis analysis of variance is presented. This formulation illustrates the parallel nature of the parametric general linear model and the Kruskal-Wallis model. Using the matrix formulation, it is shown that the Kruskal-Wallis method can be implemented on a digital computer as a special case of a general ...
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A linear generalization of Stackelberg’s model
Theory and Decision, 2008We study an extension of Stackelberg's model in which many firms can produce at many different times. Demand is affine while cost is linear. In this setting, we investigate whether Stackelberg's results in a two-firm game are robust when the number of firms increases.
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Linear and Generalized Linear Mixed Models and Their Applications
Technometrics, 2008(2008). Linear and Generalized Linear Mixed Models and Their Applications. Technometrics: Vol. 50, No. 1, pp. 93-94.
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The American Statistician, 1994 Generalized Model for Linear Referencing in Transportation
GeoInformatica, 2002Summary: The Generalized Model for Linear Referencing is proposed as a theoretical basis for representing and translating linear locations. It separates the concepts of the linear element which is being measured and the linear method of measurement. It formalizes the concept of a distance expression as the measurement which is made.
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1981
In Chapter 3 and 4 the method of maximum likelihood was introduced as a general method by which a model could be fitted to data. In Chapter 5 we specialized by restricting ourselves to normally distributed random variables, and to cases where the model is linear in the unknown parameters.
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In Chapter 3 and 4 the method of maximum likelihood was introduced as a general method by which a model could be fitted to data. In Chapter 5 we specialized by restricting ourselves to normally distributed random variables, and to cases where the model is linear in the unknown parameters.
openaire +1 more source