Statistical inference of semi-parametric varying coefficients for spatial data and its appication to survival data

2016 Fiscal Year Final Research Report

• Project/Area Number: 26330043
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Research Field: Statistical science
• Research Institution: Hiroshima University
• Principal Investigator: Satoh Kenichi 広島大学, 原爆放射線医科学研究所, 准教授 (30284219)
• Co-Investigator(Renkei-kenkyūsha): KAMO KEN-ICHI 札幌医科大学, 医療人育成センター, 准教授 (10404740)
• Project Period (FY): 2014-04-01 – 2017-03-31
• Keywords: 変化係数 / 成長曲線モデル / 回帰モデル

Outline of Final Research Achievements

In this research we developed a method for estimating the regression coefficients for a growth curve model when the time trend of the baseline has not been specified. The concept of this method is similar to that of the Cox proportional hazard model. No particular shape is assumed for the baseline time trends, or, alternatively, it can be assumed that they are estimated nonparametrically. Because of these nuisance parameters for the baseline trends,we do not have to pay attention to model those shapes. In addition to the simplicity of modeling baseline curves, we can also nonparametrically describe the baseline trends by using the residuals after the regression coefficients have been estimated.

Free Research Field

統計学