Quantiles for counts

• 成果类型: Article
• 署名作者: Machado, JAF; Silva, JMCS
• 署名单位: Universidade Nova de Lisboa; Universidade de Lisboa; Universidade de Lisboa
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214505000000330
• 发表日期: 2005
• 页码: 1226-1237
• 关键词: median regression; least-squares; models

摘要

This article studies the estimation of conditional quantiles of counts. Given the discreteness of the data, some smoothness must be artificially imposed on the problem. We show that it is possible to smooth the data in a way that allows inference to be performed using standard quantile regression techniques. The performance and implementation of the estimators are illustrated by simulations and an application.