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Introduction to Generalized Linear Models (GLM) We introduce the notion of GLM as an extension of the traditional normal-theory-based linear regression models.
This will be very helpful in order to gain a general insight into all discussions till the end of this course since the speci?c models that will be discussed in details from now all, will turn out to be speci?c GLM. We already mentioned in the introductory lecture that when dealing with categorical data as output, it is not wise to model it (or for that matter, the probabilities for its particular categories) by using linear models. This is why one has tried to extend the Linear Models theory to make it suitable for such situations. There are at least two important aspects of the extension of the traditional normal- theory based linear regression model.
The set of generalized linear models is indeed quite large. These include: classical linear models with normal errors, logistic and probit models for binary categorical data, and log- linear models for multinomial data. Many other statistical models can also be shown to bea particular GLM after choosing suitably the link function and the response probability distribution.
Collective risk models : The models applied to insurance portfolios which do not create direct reference to the risk characteristics of individual members of the portfolio when des
difference between histogram and historigram
The Null Hypothesis - H0: There is autocorrelation The Alternative Hypothesis - H1: There is no autocorrelation Rejection Criteria: Reject H0 (n-s)R 2 > = (1515 - 4) x (0.
Your first task is to realize two additional data generation functions. Firstly, extend the system to generate random integral numbers based on normal distribution. You need to stu
Influence statistics: The range of statistics designed to assess the effect or the in?uence of an observation in determining results of the regression analysis. The general approa
Uncertainty analysis is the process for assessing the variability in the outcome variable that is due to the uncertainty in estimating the values of input parameters. A sensitivit
Projection pursuit is a procedure for attaning a low-dimensional (usually two-dimensional) representation of the multivariate data, which will be particularly useful in revealing
The term used in a variety of methods in statistics, but mostly to refer to the categorical variable, with a less number of levels, under examination in an experiment as a possible
Regression discontinuity design is the quasi-experimental design in which participants in, for instance, an intervention study, are assigned to the treatment and control groups on
Bioassay : It is an abbreviation of biological assay, which in its classical form includes an experiment conducted on biological material to determine relative potency of test and
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