.png){kind=icon}
返回
Estimating joint preference: A sub-sampling approach
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ijresmar.2006.09.001.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Individual and joint preference inform about different aspects of a product's marketing strategy. While individual preference is easily measured, joint preference is expensive to obtain. The author proposes a sub-sampling approach that uses MCMC and data imputation techniques to estimate individual and joint preference. It requires individual data from the entire sample and joint data from a fraction of the sample. Empirical evidence suggests that the sub-sampling approach works well when joint data are collected from 25% of the sample. Predictive and correlation tests demonstrate the superiority of the proposed approach. Greater than 50% reduction in data collection cost is shown. (c) 2006 Elsevier B.V. All rights reserved.
Keyword:
data imputation
hierarchical Bayes
survey
sampling
group choice
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.5
论文数:
1.2K
被引数:
6.4K
机构
暂无机构信息
引用论文
Short- and long-term changes in blood miRNA levels after nanogold injection in rats—potential biomarkers of nanoparticle exposure.png)
Biomarkers
BiomarkersIF0
PREAI
分享收藏Improving parameter estimates and model prediction by aggregate customization in choice experiments
PREAI
分享收藏
查看更多内容