Hostname: page-component-586b7cd67f-t8hqh Total loading time: 0 Render date: 2024-11-27T01:48:20.470Z Has data issue: false hasContentIssue false

Factors Affecting Fire Loss—Multiple Regression Models with Extreme Values

Published online by Cambridge University Press:  29 August 2014

Rights & Permissions [Opens in a new window]

Summary

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

In his paper to the Tenth ASTIN Colloquium the author presented generalised extreme value techniques for making use of all large losses that are available for analysis and not merely the largest. In this paper the problem of assessing the relative contributions of various factors to fire losses is investigated. A model concerned with multiple regression with extreme observations of given rank is developed. It takes into consideration the biases due to the use of extremes and the differences between categories of risks in regard to the frequency of fires (or claims). By way of illustration the model was applied to the largest and second largest losses in the textile industries in the United Kingdom during the six-year period 1965 to 1970. The presence or absence of sprinklers, whether the buildings were single-storey or multi-storey, and total floor area were the independent variables included in this preliminary investigation. Judged from extreme losses sprinklers appear to reduce considerably the expected damage in all fires.

The technique enables different estimates to be obtained for each regression parameter for different ranks. It is desirable to have a single overall estimate for each parameter; and for this purpose a second model is developed for performing a regression analysis combining observations pertaining to a number of ranks. Covariances of the residual errors are also taken into account in this model.

Type
Research Article
Copyright
Copyright © International Actuarial Association 1975

References

[1]Ramachandran, G. (1974), “Extreme value theory and large fire losses”, The ASTIN Bull., Vol. VII, Pt. 3. 293310CrossRefGoogle Scholar
[2]Ramachandran, G.(1972), “Extreme value theory and fire losses—further results”, Department of the Environment and Fire Offices' Committee Joint Fire Research Organisation Fire Research Note No. 910.Google Scholar
[3]Ramachandran, G.(1973), “Factors affecting fire loss—Multiple regression model with extreme values”, Department of the Environment and Fire Offices' Committee Joint Fire Research Organisation Fire Research Note' No. 991.Google Scholar
[4]Ramachandran, G.(1970), “Fire loss indexes”, Ministry of Technology and Fire Offices' Committee Joint Fire Research Organisation Fire Research Note No. 839.Google Scholar
[5]Blandin, A.(1956), “Bases techniques de l'assurance contre l'incendie”, A. Martel.Google Scholar
[6] United Kingdom Fire Statistics. London. Her Majesty's Stationery Office. (Annual publication).Google Scholar
[7]Benkert, L. G. (1963), “The log normal model for the distribution of one claim”, The ASTIN Bull., Vol. II, Pt. 1, 923.Google Scholar
[8]Lloyd, E. H. (1952), “Least squares estimation of location and scale parameters using order statistics”, Biometrika, 39, 8895.CrossRefGoogle Scholar
[9]Nelson, W.and Hahn, G. J. (1972), “Linear estimation of a regression relationship from censored data. Part I. Simple methods and their application”, Technometrics, 14, 247269.Google Scholar
[10]Nelson, W.and Hahn, G. J. (1973), “Linear estimation of a regression relationship from censored data. Part II. Best linear unbiased estimation and theory”, Technometrics, 15, 133150.Google Scholar
[11]Teichroew, D.(1956), “Tables of expected values of order statistics and products of order statistics from samples of size 20 and less from the normal distribution”, Ann. Math. Statist., 27, 410426.CrossRefGoogle Scholar
[12]Ruben, H. (1954), “On the moments of order statistics in samples from normal population”, Biometrika, 41, 200227.CrossRefGoogle Scholar