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Entropy 2015, 17(6), 3679-3691; doi:10.3390/e17063679

Density Regression Based on Proportional Hazards Family

1 and 2,3,*
1
Business School, Shihezi University, Xinjiang, 831300, China
2
School of Management, Hefei University of Technology, Hefei, 230009, China
3
Department of Mathematics, College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge, UB8 3PH, UK
*
Author to whom correspondence should be addressed.
Academic Editors: Carlos Alberto de Bragança Pereira and Adriano Polpo
Received: 16 March 2015 / Revised: 21 May 2015 / Accepted: 29 May 2015 / Published: 4 June 2015
(This article belongs to the Special Issue Inductive Statistical Methods)
View Full-Text   |   Download PDF [261 KB, uploaded 4 June 2015]   |  

Abstract

This paper develops a class of density regression models based on proportional hazards family, namely, Gamma transformation proportional hazard (Gt-PH) model . Exact inference for the regression parameters and hazard ratio is derived. These estimators enjoy some good properties such as unbiased estimation, which may not be shared by other inference methods such as maximum likelihood estimate (MLE). Generalised confidence interval and hypothesis testing for regression parameters are also provided. The method itself is easy to implement in practice. The regression method is also extended to Lasso-based variable selection. View Full-Text
Keywords: best linear unbiased estimators (BLUE); density regression; exact inference; gamma random variable; proportional hazards distribution family; regression analysis best linear unbiased estimators (BLUE); density regression; exact inference; gamma random variable; proportional hazards distribution family; regression analysis
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Dang, W.; Yu, K. Density Regression Based on Proportional Hazards Family. Entropy 2015, 17, 3679-3691.

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