Parametric quantile regression for income data
收入数据的参数分位数回归AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
本页为预印本版本；arXiv 收录、作者填写的期刊信息以及作者既有发表经历，都不单独证明本篇已接收。作者发表记录按 OpenAlex 身份与完整姓名、本站覆盖期刊 ISSN 及正式卷期核对，只展示已核实例，档案仍可能误合并。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要明确研究收入数据的参数分位数回归及不对称分布建模，直接对应分位数与分布方向。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
收入数据的参数分位数回归
- 研究问题
- 收入数据通常正值且不对称，如何利用合适的参数分布建立分位数回归，以描述收入条件分布并开展参数推断？
- 方法与思路
- 作者将 Dagum 与 Singh–Maddala 分布按分位数参数重新参数化，建立回归模型，并用最大似然、模拟和残差分析评估。
- 材料陈述的贡献
- 摘要称给出分布性质及推断方法，模拟表现符合预期，两类模型在智利家庭收入数据上均获得较好的拟合表现。
- 阅读关注点
- 应核对分位数参数化与协变量进入方式、最大似然推断条件、残差诊断，以及模拟和实际拟合的评价指标与比较对象。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未给出拟合数值或错设下表现，不能判断其相对非参数方法的优势。
原文摘要
Univariate normal regression models are statistical tools widely applied in many areas of economics. Nevertheless, income data have asymmetric behavior and are best modeled by non-normal distributions. The modeling of income plays an important role in determining workers' earnings, as well as being an important research topic in labor economics. Thus, the objective of this work is to propose parametric quantile regression models based on two important asymmetric income distributions, namely, Dagum and Singh-Maddala distributions. The proposed quantile models are based on reparameterizations of the original distributions by inserting a quantile parameter. We present the reparameterizations, some properties of the distributions, and the quantile regression models with their inferential aspects. We proceed with Monte Carlo simulation studies, considering the maximum likelihood estimation performance evaluation and an analysis of the empirical distribution of two residuals. The Monte Carlo results show that both models meet the expected outcomes. We apply the proposed quantile regression models to a household income data set provided by the National Institute of Statistics of Chile. We showed that both proposed models had a good performance both in terms of model fitting. Thus, we conclude that results were favorable to the use of Singh-Maddala and Dagum quantile regression models for positive asymmetric data, such as income data.