Modelling of Auto Insurance Claims Using Discrete Probability Distributions
Abstract
Within general insurance, pricing of premiums is always a challenging task. Frequency of
claims plays a big part in pricing of premiums. Frequency of claims are determined by the
attributes of a particular policy holder. Count regression analysis allows one to nd out
which characteristic of a policy holder plays a signi cant role in determining the frequency
of claim and also in predicting the frequency of claims given the characteristics of a
particular policy holder. The objective of this thesis is to nd out which among the Poisson,
NB1 and NB2 models is a better t to the count data under consideration. The count data
is from Kenendia insurance. The best model is chosen based on the log-likelihood method
and the Akaike’s Information Criteria (AIC).
Publisher
University of Nairobi
Rights
Attribution-NonCommercial-NoDerivs 3.0 United StatesUsage Rights
http://creativecommons.org/licenses/by-nc-nd/3.0/us/Collections
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