Density Estimation with Confidence Sets Exemplified by Superclusters and Voids in the Galaxies
带置信集合的密度估计：星系超星系团与空洞示例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：摘要研究通用密度估计和天文学分布，未涉及微观分位数或经济分布效应。
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
带置信集合的密度估计：星系超星系团与空洞示例
- 研究问题
- 如何同时给出半参数密度的点估计与置信集合，并展示平滑程度不确定性对星系速度密度峰数判断的影响？
- 方法与思路
- 作者用样本间距拟合优度检验限定平滑参数范围，逐一优化正态混合密度，再通过交叉验证选择点估计。
- 材料陈述的贡献
- 摘要提出混合分布优化算法和密度置信集合；在星系速度示例中，集合内估计的峰数为三至七，并辅以峰数检验。
- 阅读关注点
- 应核对密度置信集合的覆盖含义、间距目标与检验校准，以及平滑范围、峰数检验和星系聚类解释之间的联系。
- 阅读边界
- 仅据OpenAlex索引摘要，稿件版本未注明；置信水平和覆盖条件未说明，示例峰数范围不能直接确认为真实星系团数量。
原文摘要
A method is presented for forming both a point estimate and a confidence set of semiparametric densities. The final product is a three-dimensional figure that displays a selection of density estimates for a plausible range of smoothing parameters. The boundaries of the smoothing parameter are determined by a nonparametric goodness-of-fit test that is based on the sample spacings. For each value of the smoothing parameter our estimator is selected by choosing the normal mixture that maximizes a function of the sample spacings. A point estimate is selected from this confidence set by using the method of cross-validation. An algorithm to find the mixing distribution that maximizes the spacings functional is presented. These methods are illustrated with a data set from the astronomy literature. The measurements are velocities at which galaxies in the Corona Borealis region are moving away from our galaxy. If the galaxies are clustered, the velocity density will be multimodal, with clusters corresponding to modes. Natural candidates for examining the distribution of the data are finite normal mixtures and histograms. The shortcomings of these methods become apparent from the analysis of these data. By finding a confidence set of densities a set of estimates is obtained, ranging from smooth to rough; the number of modes ranges from three to seven. The confidence set of densities is further substantiated by performing nonparametric tests for the number of modes.