A variable selection procedure for depth measures

AStA Advances in Statistical Analysis - We herein introduce variable selection procedures based on depth similarity, aimed at identifying a small subset of variables that can better explain the...     

Classifying Speech Sonority Functional Data using a Projected Kolmogorov-Smirnov Approach

This paper addresses a linguistically motivated question of classification of functional data, namely the statistical classification of languages according to their rhythmic features. This is an important open problem in phonology. The analysis is based on the information provided by the sonority, which is an index of local regularity of the speech signal. Our main tool is the projected Kolmogorov-Smirnov test. This is a new goodness of fit test for functional data. The result obtained supports the linguistic conjecture of the existence of three rhythmic classes.