Compromise Maximum Likelihood Estimators for Location
位置参数的折中最大似然估计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。
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：核心是通用稳健位置估计，股票收益仅作应用，未明确涉及资产定价、风险或收益预测。
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…
关注这篇论文的作者
位置参数的折中最大似然估计
- 研究问题
- 摘要研究在小样本且总体尾部形状不确定时，如何构造兼顾多个候选密度的稳健位置估计量，并评价其有限样本表现。
- 方法与思路
- 作者按样本似然对候选密度的最大似然估计加权，构造折中估计量，并用高斯与 slash 分布组合开展小样本模拟。
- 材料陈述的贡献
- 摘要称，在所考察的高斯、slash 及若干 t 分布模拟中，该组合优于比较的 M 估计与截尾均值，并展示股票组合收益应用。
- 阅读关注点
- 应核对似然权重的定义、候选密度的选择依据，以及比较采用的损失指标、调参规则和收益数据中的具体估计目标。
- 阅读边界
- 所述优势限于摘要列出的模拟设置，不能推广至所有分布或金融预测；索引未注明稿件版本，不能推断后续刊本变化。
原文摘要
This article describes a new approach for constructing robust location estimators. These estimators are constructed to be nearly optimal in small sample sizes simultaneously for two or more possible underlying shapes of the density of the data. The estimators are weighted averages of the maximum likelihood estimators for the densities considered, where the weights depend on the sample through the likelihood functions. Because of this relationship to the usual MLE's, these estimators are called compromise maximum likelihood estimators (CMLE's). For the CMLE's to exhibit good robustness properties, the densities used to construct the CMLE's should be chosen to, in a sense, “span” a reasonable range of possible underlying distributions of the data encountered in practice. For example, to construct CMLE's with the usual robustness properties, that is, which perform well both for narrow-tailed Gaussian data and for data containing outliers or from wide-tailed distributions, the CMLE's might be constructed based on the Gaussian distribution and a wide-tailed distribution such as the slash distribution. One would then expect that the CMLE's would perform well for data from all symmetric distributions with tails that are in between these two extremes. To verify that this is the case, Monte Carlo results are presented for CMLE's that compromise between the Gaussian and slash distributions for sample sizes 5, 10, and 20. Results are presented both for the Gaussian and slash distributions, which are used to construct the estimators, and for Student's t distributions with 2, 3, 5, 7, and 10 df, which have tails in between the Gaussian and slash extremes. The results show that the Gaussian-slash CMLE's perform better than the usual robust location estimators such as the M estimators and trimmed means. As an example of application of the estimators to real data, the behavior of the estimators for some stock portfolio return data is also examined.