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Actuarial Modelling of Claim Counts Risk Classification, Credibility ...

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156 <strong>Actuarial</strong> <strong>Modelling</strong> <strong>of</strong> <strong>Claim</strong> <strong>Counts</strong><br />

In the model A1–A2 <strong>of</strong> Definition 3.1, we intuitively feel that the following statements<br />

are true:<br />

Statement S1<br />

Statement S2<br />

Statement S3<br />

i ‘increases’ in the past claims N i•<br />

N iTi +1 ‘increases’ in the past claims N i•<br />

N iTi +1 and N i• are ‘positively dependent’.<br />

This section aims to precisely define the meaning <strong>of</strong> ‘increases’ in statements S1 and S2, as<br />

well as the nature <strong>of</strong> the ‘positive dependence’ involved in statement S3. The pro<strong>of</strong>s will be<br />

omitted because <strong>of</strong> their technical nature; for a detailed study, we refer the reader to Denuit<br />

ET AL. (2005, Chapter 7). Note that we present the results in terms <strong>of</strong> stochastic dominance<br />

whereas in fact the stronger (but less intuitive) likelihood ratio order applies.<br />

3.5.2 Stochastic Order Relations<br />

In order to formalize the increasingness involved in statements S1–S2, our study will<br />

extensively resort to stochastic orderings. Therefore, we recall in this section the definition <strong>of</strong><br />

stochastic dominance, as well as some intuitive intepretations. Given two random variables<br />

X and Y , X is said to be smaller than Y in the stochastic dominance, written as X ≼ ST Y ,if<br />

PrX>t≤ PrY>tfor all t ∈ <br />

We see that a ranking in the ≼ ST -sense translates the intuitive meaning <strong>of</strong> ‘being smaller<br />

than’ in probability models: indeed, we compare the probability that both random variables<br />

exceed some given threshold t, and the smallest one in the ≼ ST -sense has the smallest<br />

probability <strong>of</strong> exceeding the threshold. If M and N are two counting random variables then<br />

M ≼ ST N ⇔<br />

+∑<br />

j=k<br />

PrM = j ≤<br />

+∑<br />

j=k<br />

PrN = j for all k = 0 1<br />

One intuitively feels that a random variable N following the Poisson distribution with<br />

mean gets bigger as increases. The next implication formalizes this intuitive statement:<br />

≤ ′ ⇒ N ≼ ST N ′<br />

The oi family thus increases in its parameter in the ≼ ST -sense.<br />

3.5.3 Comparisons <strong>of</strong> Predictive Distributions<br />

We then have the following results that formalize statements S1 and S2. First, i increases<br />

in the past claims N i• in the ≼ ST -sense, that is<br />

i N i• = k ≼ ST i N i• = k ′ for k ≤ k ′ (3.18)<br />

⇔ Pr i >tN i• = k ≤ Pr i >tN i• = k ′ whatever t, provided k ≤ k ′

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