Most statistical methodology assumes that observations are normally distributed.
Yet in practice data is often non-normal, not just because of errors or outliers, but structurally. Responses may be strictly positive, skew, categorical, bounded, have exact zeros, or fail to be normal in other creative and unavoidable ways. In this course we will see that may of the properties that make the normal distribution convenient have close analogues in far more huge class of distributions, the exponential family distributions, and that this larger class of distributions allows us to handle a wide range of common situations. The course will cover the essentials of exponential family theory and in particular the theory of regression analysis with responses from an exponential family. You can think of the topic as the theory of generalized linear models, and the subject as a continuation of MS305 .