MAZEST
M- and Z-estimation in semiparametric statistics : applications in various fields
| Coordinatore | UNIVERSITE CATHOLIQUE DE LOUVAIN
Spiacenti, non ci sono informazioni su questo coordinatore. Contattare Fabio per maggiori infomrazioni, grazie. |
| Nazionalità Coordinatore | Belgium [BE] |
| Totale costo | 750˙000 € |
| EC contributo | 750˙000 € |
| Programma | FP7-IDEAS-ERC
Specific programme: "Ideas" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013) |
| Code Call | ERC-2007-StG |
| Funding Scheme | ERC-SG |
| Anno di inizio | 2008 |
| Periodo (anno-mese-giorno) | 2008-07-01 - 2014-06-30 |
Partecipanti
| # | ||||
|---|---|---|---|---|
| 1 |
UNIVERSITE CATHOLIQUE DE LOUVAIN
Organization address
address: Place De L'Universite 1 contact info |
BE (LOUVAIN LA NEUVE) | hostInstitution | 0.00 |
| 2 |
UNIVERSITE CATHOLIQUE DE LOUVAIN
Organization address
address: Place De L'Universite 1 contact info |
BE (LOUVAIN LA NEUVE) | hostInstitution | 0.00 |
Mappa
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Obiettivo del progetto (Objective)
'The area of semiparametric statistics is, in comparison to the areas of fully parametric or nonparametric statistics, relatively unexplored and still in full development. Semiparametric models offer a valid alternative for purely parametric ones, that are known to be sensitive to incorrect model specification, and completely nonparametric models, which often suffer from lack of precision and power. A drawback of semiparametric models so far is, however, that the development of mathematical properties under these models is often a lot harder than under the other two types of models. The present project tries to solve this difficulty partially, by presenting and applying a general method to prove the asymptotic properties of estimators for a wide spectrum of semiparametric models. The objectives of this project are twofold. On one hand we will apply a general theory developed by Chen, Linton and Van Keilegom (2003) for a class of semiparametric Z-estimation problems, to a number of novel research ideas, coming from a broad range of areas in statistics. On the other hand we will show that some estimation problems are not covered by this theory, we consider a more general class of semiparametric estimators (M-estimators called) and develop a general theory for this class of estimators. This theory will open new horizons for a wide variety of problems in semiparametric statistics. The project requires highly complex mathematical skills and cutting edge results from modern empirical process theory.'