Bagging prediction for censored data: Application for theatre demand

Abstract

In this research we analyze the demand for performing arts. Since the observed demand is limited by the capacity of house, one needs to account for demand censorship. The presence of consumer segments with different purposes of going to the theatre and willingness-to-pay for performance and ticket characteristics compels to account for heterogeneity in theatre demand. In this paper we propose an estimator for prediction of demand that accounts for both demand censorship and preferences heterogeneity. The estimator is based on the idea of classification and regression trees and bagging prediction aggregation. We extend the algorithm for censored data prediction problem. Our algorithm predicts and combines predictions from both discrete and continuous parts of censored data. We show that the estimator is better in prediction accuracy compared with estimators which account for censorship or heterogeneity of preferences only.