Generalized Linear Model Lecture
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چکیده
منابع مشابه
Maximum Likelihood Estimation of Parameters in Generalized Functional Linear Model
Sometimes, in practice, data are a function of another variable, which is called functional data. If the scalar response variable is categorical or discrete, and the covariates are functional, then a generalized functional linear model is used to analyze this type of data. In this paper, a truncated generalized functional linear model is studied and a maximum likelihood approach is used to esti...
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Introduction Selection the appropriate statistical model for the response variable is one of the most important problem in the finite mixture of generalized linear models. One of the distributions which it has a problem in a finite mixture of semi-parametric generalized statistical models, is the Poisson distribution. In this paper, to overcome over dispersion and computational burden, finite ...
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In this paper, we consider an i.e., multiple step-stress accelerated life testing (ALT) experiment with unequal duration of time . It is assumed that the time to failure of a product follows Rayleigh distribution with a log-linear relationship between stress and lifetime and also we assume a generalized Khamis-Higgins model for the effect of changing stress levels. Taking into account that the...
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In some applications, the response variable assumes values in the unit interval. The standard linear regression model is not appropriate for modelling this type of data because the normality assumption is not met. Alternatively, the beta regression model has been introduced to analyze such observations. A beta distribution represents a flexible density family on (0, 1) interval that covers symm...
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Departement Elektrotechniek ESAT-SISTA/TR 96-71 Generalized Linear Complementarity Problems and the Analysis of Continuously Variable Systems and Discrete Event Systems1 Bart De Schutter2 and Bart De Moor2 Proceedings of the International Workshop on Hybrid and Real-Time Systems (HART'97), Grenoble, France, March 26-28, 1997 vol. 1201 in Lecture Notes in Computer Science, Springer-Verlag, pp. 4...
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