allfeeds.ai

 

Mathematik, Informatik und Statistik - Open Access LMU - Teil 03/03  

Mathematik, Informatik und Statistik - Open Access LMU - Teil 03/03

Language: de

Genres: Education

Contact email: Get it

Feed URL: Get it

iTunes ID: Get it


Get all podcast data

Listen Now...

A General Framework for the Selection of Effect Type in Ordinal Regression 1/2
Monday, 18 January, 2016

In regression models for ordinal response, each covariate can be equipped with either a simple, global effect or a more flexible and complex effect which is specific to the response categories. Instead of a priori assuming one of these effect types, as is done in the majority of the literature, we argue in this paper that effect type selection shall be data-based. For this purpose, we propose a novel and general penalty framework that allows for an automatic, data-driven selection between global and category-specific effects in all types of ordinal regression models. Optimality conditions and an estimation algorithm for the resulting penalized estimator are given. We show that our approach is asymptotically consistent in both effect type and variable selection and possesses the oracle property. A detailed application further illustrates the workings of our method and demonstrates the advantages of effect type selection on real data.

 

We also recommend:


Effortless English Podcast | Learn English with AJ Hoge
AJ Hoge

Collaboration Nation R08
Paul Bogush

The Republic by Plato presented by ejunto.org
Andrew Julow

int2 fizzics
Sinclair Mackenzie

Lori Keller's Podcast
Lori Keller

EconTalk Archives, 2009
EconTalk: Russ Roberts, Library of Economics and Liberty

Business problem solving and improvement - for iPod/iPhone
The Open University

Environment: LA River - for iPod/iPhone
The Open University

Somerville College
Oxford University

Epidemics and Vaccines
Oxford University

Lagrange Point
Lagrange Point


Fly with Lily