#### Filter Results:

#### Publication Year

2014

2016

#### Co-author

#### Key Phrase

#### Publication Venue

Learn More

— Load forecasting of energy demand is usually focused on mean values in related statistical models and ignores rare peak events. This paper provides Extreme Value Theory analysis of the peak events in electrical power load demand. It estimates risk of the peak events by combining forecast of the mean with extreme value modeling of distribution tail. The… (More)

— This paper develops a method for building non-parametric stochastic models of multivariate distributions from large data sets. The motivation is stochastic optimization based on time series forecasting models. The proposed non-parametric stochastic modeling approach is based on multiple quantile regressions with inter-quantile smoothing. The models are… (More)

—This paper develops a method for estimating trends of extreme events statistics across multiple time periods. Some of the periods might have no extreme events and some might have much data. The extreme event distribution is modeled with a Pareto or exponential tail. The method requires selecting an extreme event threshold and then solving two convex… (More)

— This paper develops a statistical modeling and estimation approach combining robust regression and long tail estimation. The approach can be considered as a generalization of Huber regression in robust statistics. A mixture of asymmet-ric Laplace and Gaussian distributions is estimated using an EM algorithm. The approach estimates the regression model,… (More)

—This paper presents an efficient computational methodology for longitudinal and cross-sectional analysis of extreme event statistics in large data sets. The analyzed data are available across multiple time periods and multiple individuals in a population. Some of the periods and individuals might have no extreme events and some might have much data. The… (More)

- ‹
- 1
- ›