Loss Data Analysis With Maximum Entropy

This volume deals with two complementary topics. On one hand the book deals with the problem of determining the the probability distribution of a positive compound random variable, a problem which appears in the banking and insurance industries, in many areas of operational research and in reliability problems in the engineering sciences. On the other hand, the methodology proposed to solve such problems, which is based on an application of the maximum entropy method to invert the Laplace transform of the distributions, can be applied to many other problems. The book contains applications to a large variety of problems, including the problem of dependence of the sample data used to estimate empirically the Laplace transform of the random variable. Contents Introduction Frequency models Individual severity models Some detailed examples Some traditional approaches to the aggregation problem Laplace transforms and fractional moment problems The standard maximum entropy method Extensions of the method of maximum entropy Superresolution in maxentropic Laplace transform inversion Sample data dependence Disentangling frequencies and decompounding losses Computations using the maxentropic density Review of statistical procedures

We present some results of the application of maximum entropy methods to determine the probability density of compound random variables. This problem is very important in the banking and insurance business, but also appears in system reliability and in operations research. The mathematical tool consists of inverting Laplace transforms of positive compound random variables using the maximum entropy method. This method needs a very small number of (real) values of the Laplace transform, is robust, works with small data sets, and it can be extended to include errors in the data as well as data specified up to intervals. In symbols, the basic typical problem consist in determining the density f S of a compound random variable like S = ∑ Nn=1 X n , or that of a sum of such random variables. There, N is an integer random variable and then X n are a sequence of positive, continuous random variables, independent among themselves and of N. Our methodology can be applied to determine the probability density of the total loss S and that of the individual losses.

The analysis of loss data is of utmost interest in many branches of the financial and insurance businesses, in structural engineering and in operations research among others. In the financial industry the determination of the distribution of losses is the first step to take in order to compute regulatory risk capitals, in insurance we need the distribution of losses in order to determine the risk premia. In reliability analysis one needs to determine the distribution of accumulated damage or the first time of occurrence of a composite event, and so on. Not only that, in some cases we have data on the aggregate risk but we happen to be interested in determining the statistical nature of the different types of events that contribute to the aggregate loss.Even though in many of these branches of activity one may have good theoretical descriptions of the underlying processes, the nature of the problems is such that we must resort to numerical methods to actually compute the loss distributions. Besides of being able to determine numerically the distribution of losses, we also need to assess the dependence of the distribution of losses and that of the quantities computed with it, on the empirical data.It is the purpose of this note to illustrate the how the maximum entropy method and its extensions can be used for deal with the various issues that come up in the computation of the distribution of losses.

This Is The First Comprehensive Book About Maximum Entropy Principle And Its Applications To A Diversity Of Fields Like Statistical Mechanics, Thermo-Dynamics, Business, Economics, Insurance, Finance, Contingency Tables, Characterisation Of Probability Distributions (Univariate As Well As Multivariate, Discrete As Well As Continuous), Statistical Inference, Non-Linear Spectral Analysis Of Time Series, Pattern Recognition, Marketing And Elections, Operations Research And Reliability Theory, Image Processing, Computerised Tomography, Biology And Medicine. There Are Over 600 Specially Constructed Exercises And Extensive Historical And Bibliographical Notes At The End Of Each Chapter.The Book Should Be Of Interest To All Applied Mathematicians, Physicists, Statisticians, Economists, Engineers Of All Types, Business Scientists, Life Scientists, Medical Scientists, Radiologists And Operations Researchers Who Are Interested In Applying The Powerful Methodology Based On Maximum Entropy Principle In Their Respective Fields.

One of the main problems in the advanced measurement approach to determine operational risk regulatory capital, consists of the computation of the distribution of losses when the data is made up of aggregated losses caused by different types of risk events in different business lines. A similar problem appears in the insurance industries when there is need of aggregating losses of different types. When the data is well collected, that is, when the losses are collected as a joint vector, the maxentropic techniques are quite suitable for finding the probability density of the aggregated loss. When the data is not well collected, the maxentropic procedure provides us with the marginal densities, which can then be coupled by means of some appropriate copula, and then carry on one of the two procedures that we apply. At any rate, the two possibilities hinge in an essential way on the maxentropic technique to determine a probability density from its Laplace transform. This is due to the fact that such techniques provide us with analytical expressions for the densities, which makes many numerical procedures easier to implement. It is the aim of this note, to examine and compare these alternative ways of solving the problem of determining the density of aggregate losses.

The problem of determining probability densities from data is important in many fields, in particular in insurance and risk analysis. The method of maximum entropy has proven to be a powerful tool to determine probability densities from a few values of its Laplace transform. This is so, even when the amount of data to compute numerically the Laplace transform is small. But in this case the variability of the reconstruction due to the sample variability in the available data can lead to quite different results. It is the purpose of this note to explore and quantify as much as possible the variability of the densities reconstructed by means of two maxentropic methods: the standard maximum entropy method and its extension to incorporate data with errors. The issues that we consider are of special interest for the advanced measurement approach (AMA) in operational risk, which is based on loss data analysis (LDA) to determine regulatory capital, as well as to determine the loss distribution of risks that occur with low frequency.

Entropies and entropy-like quantities play an increasing role in modern non-linear data analysis. Fields that benefit from this application range from biosignal analysis to econophysics and engineering. This issue is a collection of papers touching on different aspects of entropy measures in data analysis, as well as theoretical and computational analyses. The relevant topics include the difficulty to achieve adequate application of entropy measures and the acceptable parameter choices for those entropy measures, entropy-based coupling, and similarity analysis, along with the utilization of entropy measures as features in automatic learning and classification. Various real data applications are given.

This monograph is an outgrowth of a set of lecture notes on the maximum entropy method delivered at the 1st Venezuelan School of Mathematics. This yearly event aims at acquainting graduate students and university teachers with the trends, techniques and open problems of current interest. In this book the author reviews several versions of the maximum entropy method and makes its underlying philosophy clear.