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An Introduction to Generalized Linear Models
Technometrics, 2002(2002). An Introduction to Generalized Linear Models. Technometrics: Vol. 44, No. 4, pp. 406-407.
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Introducing the generalized linear model: general linear model
2019This chapter reviews the generalized linear model (GLZM), which is an extremely useful and increasingly popular framework approach to analysing data. Since it relies on making assumptions about the distribution of data, it is parametric. In particular, the chapter looks at the general linear model (GLM), a sub-framework of the generalized linear model ...
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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.
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