Informative g-Priors for Logistic Regression
نویسندگان
چکیده
منابع مشابه
g - priors for Linear Regression
where X is the design matrix, ∼ N (0, σI), and β ∼ N (β0, gσ(XTX)−1). The prior on σ is the Jeffreys prior, π(σ) ∝ 1 σ2 , and usually, β0 is taken to be 0 for simplification purposes. The appeal of the method is that there is only one free parameter g for all linear regression. Furthermore, the simplicity of the g-prior model generally leads to easily obtained analytical results. However, we st...
متن کاملLogistic regression with weight grouping priors
A generalization of the commonly used Maximum Likelihood based learning algorithm for the logistic regression model is considered. It is well known that using the Laplace prior (L1 penalty) on model coefficients leads to a variable selection effect, when most of the coefficients vanish. It is argued that variable selection is not always desirable; it is often better to group correlated variable...
متن کاملExploiting Informative Priors for Bayesian Classification and Regression Trees
A general method for defining informative priors on statistical models is presented and applied specifically to the space of classification and regression trees. A Bayesian approach to learning such models from data is taken, with the MetropolisHastings algorithm being used to approximately sample from the posterior. By only using proposal distributions closely tied to the prior, acceptance pro...
متن کاملPartially Improper Gaussian Priors for Nonparametric Logistic Regression
A \partially improper" Gaussian prior is considered for Bayesian inference in logistic regression. This includes generalized smoothing spline priors that are used for nonparametric inference about the logit, and also priors that correspond to generalized random e ect models. Necessary and su cient conditions are given for the posterior to be a proper probability measure, and bounds are given fo...
متن کاملHighly Informative Priors
SUMMARY After discussing the role of prior information in statistical inference, historically and in current problems, we analyze the problem of seasonal adjustment i n economics. Litterman 1980 has shown how informative priors for autoregressive coeecients can improve economic forecasts. We nd that in seasonal adjustment informative priors can have a m uch greater eeect on our conclusions. In ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Bayesian Analysis
سال: 2014
ISSN: 1936-0975
DOI: 10.1214/14-ba868