terça-feira, novembro 18, 2008

Galeria de Heróis - Ole Barndorff-Nielsen

Among Barndorff-Nielsen's early scientific contributions are his work on exponential families and on the foundations of statistics, in particular sufficiency and conditional inference. In 1977 he introduced the hyperbolic distribution as a mathematical model of the size-distribution of sand grains, formalising heuristic ideas proposed by Ralph Alger Bagnold. He also derived the larger class of generalised hyperbolic distributions. These distributions, in particular the normal-inverse Gaussian (NIG) distribution, have later turned out to be useful in many other areas of science, in particular turbulence and finance. The NIG-distribution is now widely used to describe the distribution of returns from financial assets. Later Barndorff-Nielsen played a leading role in the application of differential geometry to investigate statistical models. Another main contribution is his work on asymptotic methods in statistics, not least his formula for the conditional distribution of the maximum likelihood estimator given an ancillary statistic that generalizes a formula by Ronald A. Fisher (originally called the p * -formula, but now known as the Barndorff-Nielsen formula). He has jointly with David Cox written two influential books on asymptotic techniques in statistics. Since the mid-90s Barndorff-Nielsens has worked on stochastic models in finance and turbulence, on statistical methods for the analysis of data from experiments in quantum physics, and has contributed to the theory of Levy processes.

Quase todas as contribuições importantes em estatística, e em especial, econometria de finanças tem um toque do Barndorff-Nielsen. E ele continua publicando artigos fundamentais, como toda a literatura de volatilidade realizada.

1 Comments:

Anonymous Lilian said...

Dica de leitura...Textos ácidos e sarcásticos, pra quem quer ficar por dentro dos assuntos políticos e dos últimos acontecimentos de forma leve.


www.mosaicodelama.blogspot.com

Boa leitura!


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11:32 AM  

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