- Series
- Stochastics Seminar
- Time
- Thursday, January 26, 2012 - 3:05pm for 1 hour (actually 50 minutes)
- Location
- Skiles 006
- Speaker
- Jon Hosking – IBM Research Division, T. J. Watson Research Center
- Organizer
- Liang Peng
L-moments are expectations of certain linear combinations of order
statistics. They form the basis of a general theory which covers the
summarization and description of theoretical probability distributions,
the summarization and description of observed data samples, estimation
of parameters and quantiles of probability distributions, and hypothesis
tests for probability distributions. L-moments are in analogous to the
conventional moments, but are more robust to outliers in the data and
enable more secure inferences to be made from small samples about an
underlying probability distribution. They can be used for estimation
of parametric distributions, and can sometimes yield more efficient
parameter estimates than the maximum-likelihood estimates. This talk
gives a general summary of L-moment theory and methods, describes some
applications ranging from environmental data analysis to financial risk
management, and indicates some recent developments on nonparametric
quantile estimation, "trimmed" L-moments for very heavy-tailed
distributions, and L-moments for multivariate distributions.