Generalized Linear Models: While least squares regression is useful for modeling continuous real valued data generated from a Gaussian distribution. This is ...
May 14, 2016 · We consider a limiting case of generalized linear modeling when the target variables are only known up to permutation, and explore how this ...
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aggregation is the most widely used technique [5]. It is common for agencies to report both individual level information for non-sensitive attributes together
Bhowmik et al. (2016) proposed an iterative algorithm for estimating Generalised Linear Models (GLMs) when the response variables are aggregated into ...
The purpose is to show and comment the R code used for the simulations, graphs and tables shown in the article.
Feb 19, 2018 · My understanding is that using aggregated data with a poisson GLM will not effect the coefficient estimates.
We compare the GLM-Poisson results of the original data with models of the combined observations where the multiplicity or aggregation is given by weights or ...
The use of these efficient algorithms allow us to avoid possible storage problems and to speed up the computational time of the model estimation. We illustrate ...
Generalised linear models (GLM) appear to be a tool that has become very popular and have shown to be effective in the actuarial work over the past decade, ...
This thesis develops an alternative approach to modelling the expected loss cost of an insurance portfolio that allows for dependence between the frequency ...