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Title: Some statistical aspects of the estimation of fire losses.
Author: Rutstein, Ronald.
ISNI:       0000 0001 3542 4741
Awarding Body: Birkbeck (University of London)
Current Institution: Birkbeck (University of London)
Date of Award: 1983
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This study arose from a problem in which the author \.,ras required to estimate the overall mean fire loss using a sample of censored data. The OIUY information available was the total number of fires and the average large loss. A method, which can be described as a HomentQuantile method, was developed to solve the original problem. The H~ method was convenient to use as the estimates could be read from tables, and the method appeared to give good results. This study \o,Jas undertaken to investigate why the W~ method had worked so well. The chaxacteristics of the l"lq estimator are examined hereunder a Hide range of conditions - different degrees of skewness, different aL'10unts of censorship and assuming either a Lognormal or Gamma parent distribution. The HQ estimator is compared with the Haximum Likelihood (hL) estim:':-ltor. The two estimators are examined al.gebraically and these results are supplemented by a Nonte Carlo simulation. It is found that the IvlQ and HL estimators of th'2 Lognormal mean perform very differently in different circumstances. In the area of the original study - highly skewed data,high censorship and small sa.mple sizes - the }·tS':':: of the H~ estimate is much less than that of the V;L estimate. For large sarnples and very highly skewed data the HL estimate is more efficient. In the case of the Gamma mean the HQ and I'lL methods produce very similar estimates. The reasons for the different bebAviour of the estimators are . explained. It is concluded that for estimating overall fire losses the simple ML estimator is unsatisfactory if the Lognorma]. is assumed. The NQ Lognormal estimator and HL or MQ Gamma estimator appear to be acceptable. A kno\dedge of the distribution of small fire losses (about which the author cannot obtain any information) is necessary to choose the best estimator"
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Statistics