Selection of Credibility Regression Models
نویسندگان
چکیده
منابع مشابه
Robust Regression Credibility Models for Heavy-Tailed Claims
In actuarial practice, regression models serve as a popular statistical tool for analyzing insurance data and tariff ratemaking. In this paper, we consider classical credibility models that can be embedded within the framework of mixed linear models. For inference about fixed effects and variance components, likelihood-based methods such as (restricted) maximum likelihood estimators are commonl...
متن کاملAn Overview of the New Feature Selection Methods in Finite Mixture of Regression Models
Variable (feature) selection has attracted much attention in contemporary statistical learning and recent scientific research. This is mainly due to the rapid advancement in modern technology that allows scientists to collect data of unprecedented size and complexity. One type of statistical problem in such applications is concerned with modeling an output variable as a function of a sma...
متن کاملVariable Selection for Regression Models
A simple method for subset selection of independent variables in regression models is proposed. We expand the usual regression equation to an equation that incorporates all possible subsets of predictors by adding indicator variables as parameters. The vector of indicator variables dictates which predictors to include. Several choices of priors can be employed for the unknown regression coeecie...
متن کاملCredibility Models
We present a general hierarchical Bayesian model where Intelligence Sources make Reports about events or states in the world, which we call Hypotheses. The underlying multi-entity Bayes net for even a simple scenario has hundreds of nodes. We hide the details via Wigmore diagrams and a Google Maps GUI. Our application domain is Intelligence data fusion in asymmetrical warfare (terrorism). Some ...
متن کاملHybrid Regression-Classification Models for Algorithm Selection
Many state of the art Algorithm Selection systems use Machine Learning to either predict the run time or a similar performance measure of each of a set of algorithms and choose the algorithm with the best predicted performance or predict the best algorithm directly. We present a technique based on the well-established Machine Learning technique of stacking that combines the two approaches into ...
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: ASTIN Bulletin
سال: 1999
ISSN: 0515-0361,1783-1350
DOI: 10.2143/ast.29.2.504614