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Research Seminar Series in Statistics and Mathematics

Wirtschaftsuniversität Wien, Departments 4 D4.4.00809:00 - 10:30

Type Lecture / discussion
LanguageEnglish
SpeakerPeter Filzmoser (Institute of Statistics and Mathematical Methods in Economics, TU Wien)
Organizer Institut für Statistik und Mathematik
Contact katrin.artner@wu.ac.at

Peter Filzmoser (In­sti­tute of Stat­ist­ics and Mathem­at­ical Meth­ods in Eco­nom­ics, TU Wien) about “Ro­bust and sparse es­tim­a­tion meth­ods for lin­ear and lo­gistic re­gres­sion in high di­men­sions”

The In­sti­tute for Stat­ist­ics and Mathem­at­ics (De­part­ment of Fin­ance, Ac­count­ing and Stat­ist­ics) cor­di­ally in­vites every­one in­ter­ested to at­tend the talks in our Re­search Sem­inar Ser­ies, where in­ter­na­tion­ally renowned schol­ars from lead­ing uni­versit­ies present and dis­cuss their (work­ing) pa­pers.

The list of talks for the sum­mer term 2018 is avail­able via the fol­low­ing link:
Sum­mer Term 2018

Ab­stract:

The elastic net es­tim­ator has been in­tro­duced for dif­fer­ent mod­els, such as for lin­ear and lo­gistic re­gres­sion. We pro­pose a ro­bust ver­sion of this es­tim­ator based on trim­ming. It is shown how out­lier­-­free data sub­sets can be iden­ti­fied and how ap­pro­pri­ate tun­ing para­met­ers for the elastic net pen­al­ties can be se­lec­ted. A fi­nal re­weight­ing step is pro­posed which im­proves the stat­ist­ical ef­fi­ciency of the es­tim­at­ors. Sim­u­la­tions and data examples un­der­line the good per­form­ance of the newly pro­posed method, which is avail­able in the R pack­age en­etLTS on CRAN.



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