Convergence of Probability Measures. By P. Billingsley. Chichester, Sussex, Wiley, 1968. xii, 253 p. 9 1/4“. 117s.

Convergence of Probability Measures

(2002). Modelling Extremal Events for Insurance and Finance. Journal of the American Statistical Association: Vol. 97, No. 457, pp. 360-360.

Modelling Extremal Events for Insurance and Finance

Stable non-Gaussian random processes , by G. Samorodnitsky and M. S. Taqqu. Pp. 632. £49.50. 1994. ISBN 0-412-05171-0 (Chapman and Hall).

Medical genetics was revolutionized during the 1980s by the application of genetic mapping to locate the genes responsible for simple Mendelian diseases. Most diseases and traits, however, do not follow simple inheritance patterns. Geneticists have thus begun taking up the even greater challenge of the genetic dissection of complex traits. Four major approaches have been developed: linkage analysis, allele-sharing methods, association studies, and polygenic analysis of experimental crosses. This article synthesizes the current state of the genetic dissection of complex traits—describing the methods, limitations, and recent applications to biological problems.

Genetic Dissection of Complex Traits

http://cs-people.bu.edu/evimaria/cs565/achlioptas.pdf

Database-friendly random projections: Johnson-Lindenstrauss with binary coins

Computational formulas are given for stable densities and distribution functions in Zolotarev'(M) parameterization. These formulas are used in a software package called STABLE to calculate general stable densities, distribution functions and quantiles

Numerical calculation of stable densities and distribution functions

A program for maximum likelihood estimation of general stable parameters is described. The Fisher information matrix is computed, making large sample estimation of stable parameters a practical tool. In addition, diagnostics are developed for assessing the stability of a data set. Applications to simulated data, stock price data, foreign exchange rate data, radar data, and ocean wave energy are presented.

Maximum likelihood estimation and diagnostics for stable distributions

Stable Distributions

Parameterizations and modes of stable distributions

Stable distributions with elliptical contours are a class of distributions that are useful for modeling heavy tailed multivariate data. This paper describes the theory of such distributions, presents formulas for calculating their densities, and methods for fitting the data and assessing the fit. Efficient numerical routines are implemented and evaluated in simulations. Applications to data sets of a financial portfolio with 30 assets and to a bivariate radar clutter data set are presented.

John P. Nolan

Papers

Numerical calculation of stable densities and distribution functions

Maximum likelihood estimation and diagnostics for stable distributions

Stable Distributions

Parameterizations and modes of stable distributions

Multivariate elliptically contoured stable distributions: theory and estimation