摘要翻译:
本文提出了一个二元纵向数据设置中的分位数回归框架。设计了一种新的马尔可夫链蒙特卡罗(MCMC)方法来拟合该模型,并在仿真研究中证明了其计算效率。所提出的方法是灵活的,因为它可以考虑共同的和个体特定的参数,以及与几个协变量相关的多变量异质性。该方法被应用于研究美国女性劳动力参与和住房所有权。这些结果为政策制定者和研究人员都感兴趣的各种分位数提供了新的见解。
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英文标题:
《Estimation and Applications of Quantile Regression for Binary
Longitudinal Data》
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作者:
Mohammad Arshad Rahman and Angela Vossmeyer
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最新提交年份:
2019
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分类信息:
一级分类:Economics 经济学
二级分类:Econometrics 计量经济学
分类描述:Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论,微观计量经济学,宏观计量经济学,通过新方法发现的经济关系的实证内容,统计推论应用于经济数据的方法论方面。
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一级分类:Statistics 统计学
二级分类:Methodology 方法论
分类描述:Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计,调查,模型选择,多重检验,多元方法,信号和图像处理,时间序列,平滑,空间统计,生存分析,非参数和半参数方法
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英文摘要:
This paper develops a framework for quantile regression in binary longitudinal data settings. A novel Markov chain Monte Carlo (MCMC) method is designed to fit the model and its computational efficiency is demonstrated in a simulation study. The proposed approach is flexible in that it can account for common and individual-specific parameters, as well as multivariate heterogeneity associated with several covariates. The methodology is applied to study female labor force participation and home ownership in the United States. The results offer new insights at the various quantiles, which are of interest to policymakers and researchers alike.
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PDF链接:
https://arxiv.org/pdf/1909.05560