統計学輪講（第６回）

統計学輪講（第6回）
日時      2013年05月21日(火)    14時50分～16時30分
場所      経済学部新棟３階第３教室
講演者    Aurélie Boisbunon（JSPS外国人特別研究員）
演題：Model Selection: a decision-theoretic approach

概要
In this work we address the problem of model selection in the linear regressionframework. 
The objective is to determine the best predictive model based on observed data, 
that is, the model realizing the best tradeoff between goodness of fit and complexity. 
Our main contribution consists in deriving model evaluation criteria based on tools 
from Decision Theory, in particular loss estimation. Such criteria rely on a distributional 
assumption larger than the classical Gaussian one, relaxing the independence assumption 
and thus bringing robustness. We also propose a method for comparing model evaluation criteria 
through a Mean-Squared Error type measure. Our second contribution tackles the problem of 
constructing the models we compare. The collections of models considered here are obtained 
from sparse regularization methods (Lasso-type). We compare our propositions to the literature 
through a numerical study, in which we verify the quality of the selection.