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Semiparametric Generalized Linear Models: Bayesian Approaches
| Content Provider | Scilit |
|---|---|
| Author | Dey, Dipak K. Ghosh, Sujit K. Mallick, Bani K. |
| Copyright Year | 2000 |
| Description | Regression techniques are among some of the most widely used methods in applied statistics. Given a response variable Y, and a set of covariates X = (X1 , X 2 , · · ·, Xp), one is often interested in estimating an assumed functional relationship between Y and X, and in predicting further responses for new values of the covariates. One way of modeling such a relationship is to present the expected value of Y as E(YIX) = p(X), where, in general, p(·) is an unknown function of the covariates. In practice, however, p(-) is usually approximated by a simple parametric function ¢(-; /3), where f3 = ({31 , · · ·, f3r) denotes a vector of unknown parameters. The function ¢( ·; /3) is then treated as if it were the true underlying function p(-), so the problem is reduced to that of estimating {3. Furthermore, in most applications the probability distribution of the response Y is assumed to belong to an exponential familty. This gives rise to the important class of generalized linear models (GLM) (Neider and Wedderburn, 1972; McCullagh and Neider, 1989), which we shall find convenient to describe as follows. Book Name: Generalized Linear Models |
| Related Links | https://content.taylorfrancis.com/books/download?dac=C2006-0-08981-9&isbn=9780429182402&doi=10.1201/9781482293456-20&format=pdf |
| Ending Page | 248 |
| Page Count | 14 |
| Starting Page | 235 |
| DOI | 10.1201/9781482293456-20 |
| Language | English |
| Publisher | Informa UK Limited |
| Publisher Date | 2000-05-25 |
| Access Restriction | Open |
| Subject Keyword | Book Name: Generalized Linear Models Statistics and Probability Function Linear Models Exponential Semiparametric Generalized Linear Neider |
| Content Type | Text |
| Resource Type | Chapter |
