Improving risk classification and ratemaking using mixture‐of‐experts models with random effects

Classification Ratemaking Using Decision Trees

Introduction Manual rating of specific risks begin with a base rate, which is then modified by appropriate relativity factors depending on characteristics of each risk. Classical methods of deriving indicated relativities, are described by McClenahan (1996) and Finger (1996). A number of different modeling procedures are described in Brown's (1988) "minimum bias" paper and Venter's (1990) revie...

Parameter Estimation in Spatial Generalized Linear Mixed Models with Skew Gaussian Random Effects using Laplace Approximation

 Spatial generalized linear mixed models are used commonly for modelling non-Gaussian discrete spatial responses. We present an algorithm for parameter estimation of the models using Laplace approximation of likelihood function. In these models, the spatial correlation structure of data is carried out by random effects or latent variables. In most spatial analysis, it is assumed that rando...

Bayesian Determination of Sample Size in Longitudinal Studies with Binary Responses Using Random Effects Models

Sample size determination is important in all statistical studies including longitudinal studies. This is usually done by considering a target power to reduce the costs of sampling. Choosing the right sample size using efficient methods, ensures that the researcher achieve goal of the study, by spending the least amount of energy, time and money. In this article, using a method based on simulat...

Nonlife ratemaking and risk management with bayesian additive models for location, scale and shape

Generalized additive models for location, scale and shape define a flexible, semiparametric class of regression models for analyzing insurance data in which the exponential family assumption for the response is relaxed. This approach allows the actuary to include risk factors not only in the mean but also in other parameters governing the claiming behavior, like the degree of residual heterogen...

" Net-time and Conflation: Improving Classification Models with Ablated Input"