Applications of the gibbs sampling algorithm to multiple outlier detection in econometric models - the masking problem

Objective

The proposal of this project is the research and development of new methods to identify groups of outliers in econometric models. When multiple outliers exist, the standard detection statistics for a single outlier are not reliable: they may not identify groups of outliers and they may identify as outliers data that are not. This is the masking problem. The new procedures will be develop from the Bayesian point of view and using Markov Chain Monte Carlo (MCMC) methods.

Programme(s)

• FP4-TMR - Specific research and technological development programme in the field of the training and mobility of researchers, 1994-1998

Topic(s)

• 0302 - Post-doctoral research training grants
• TS99 - Other Economic, Social and Human Sciences

Funding Scheme

RGI - Research grants (individual fellowships)

Coordinator

UNIVERSITE CATHOLIQUE DE LOUVAIN

Address

34,Voie Du Roman Pays 34
1348 Louvain-la-neuve
Belgium

Participants (1)

Not available

Spain