Linear regression for numeric symbolic variables: an ordinary least squares approach based on Wasserstein Distance

Details Linear regression for numeric symbolic variables: an ordinary least squares approach based on Wasserstein Distance Linear regression for numeric symbolic variables: an ordinary least squares approach based on Wasserstein Distance

2012-02-07

arxiv.org/abs/1202.1436v1

Statistics

Methodology

Scientific paper

In this paper we present a linear regression model for modal symbolic data. The observed variables are histogram variables according to the definition given in the framework of Symbolic Data Analysis and the parameters of the model are estimated using the classic Least Squares method. An appropriate metric is introduced in order to measure the error between the observed and the predicted distributions. In particular, the Wasserstein distance is proposed. Some properties of such metric are exploited to predict the response variable as direct linear combination of other independent histogram variables. Measures of goodness of fit are discussed. An application on real data corroborates the proposed method.

Affiliated with

Also associated with

No associations

Rating

Linear regression for numeric symbolic variables: an ordinary least squares approach based on Wasserstein Distance does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Linear regression for numeric symbolic variables: an ordinary least squares approach based on Wasserstein Distance, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Linear regression for numeric symbolic variables: an ordinary least squares approach based on Wasserstein Distance will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-579328