跳到正文
收录说明
收录范围
保留计量方法研究、相关统计基础,以及实质涉及计量方法的应用、教程、比较和综述。书评、刊务及明确无关内容不公开;身份或相关性证据不足的记录保留待核,补充可靠材料后可重新判断。
当前期刊来源见下方官网链接及期刊目录。综合经济学与金融期刊逐篇筛查计量方向,证据不足时暂不公开。正刊、在线优先论文和已核实接收稿合并展示;按标题、可靠摘要、目录及身份匹配的出版社 JEL 分类核验相关性;计量类 JEL 与摘要相互印证,无 JEL 不直接排除。历史回补以可取得的注册题录和目录为准,不等于覆盖全部纸本。
日期与排序
默认按最近发表排序:正刊使用刊期,待刊使用上线日期;缺少上线日期时,排序参考登记或首次收录时间,并在卡片注明。更多筛选可选择出版状态和日期口径;卷期依据出版社元数据,不因待刊时间较长而推定已编入正刊。仅有月份或年份时,日期区间相交也会纳入。

日期精度:到日 0 篇 · 到月 4 篇 · 到年 0 篇

分类与引用
领域和方法依据题名、摘要初步分类,可人工修正;分类不代表论文质量。引用数采用 OpenAlex 单条关联记录,不合计版本。
更新与 AI 解读
正常题录、可靠摘要与引用同步继续;日、周、月简报按核实的发表与修订日期汇总,历史补录保留在文献库。AI 解读依据可靠摘要或已核实原文片段,已有解读保留供阅读参考;缺少可靠材料的条目保持待解读,浏览页面不会触发生成。
4 篇论文
导出

Solutions to Problems Posed in Volume 20(1) and 20(2): 04.2.1. A Range Equality for Block Matrices with Orthogonal Projectors—Solution

正交投影分块矩阵的值域等式:问题 04.2.1 的解答

This solution offers additional insights into the theory of block-tridiagonal Toeplitz matrices. Block Toeplitz matrices have constant blocks on each block diagonal parallel to the block main diagonal. A block partitioned matrix is said to be block-tridiagonal if the nonzero blocks occur only on the block subdiagonal, the block main diagonal, and the block superdiagonal. Block-tridiagonal Toeplitz matrices are particularly nice in that they are inexpensive to investigate.…

AI 解读阅读原文 ↗BibTeX ↓

02.4.2. On the Rank of a Matrix Useful in Goodness-of-Fit Testing of Structural Equation Models —Solution

02.4.2 结构方程模型拟合优度检验中一个矩阵的秩:解答

On the rank of a matrix useful in goodness-of-fit testing of structural equation models—solution.An excellent solution was independently proposed by A. Satorra and H. Neudecker, the posers of the problem.

AI 解读阅读原文 ↗BibTeX ↓

02.1.2. A Particular Symmetric Idempotent Matrix—Solution

02.1.2 特定对称幂等矩阵问题:解答

It is easy to see that Problem 02.1.2 holds more generally in that the result is valid for B Hermitian complex (rather than “symmetric real”), for m = 0,1,…, q (rather than just for “ m < q ”), for r ≠ 0 (rather than just for “integer r > 1”), and for tr( B k ) = tr( C k ), k = 1,2,3,4 (rather than for “ k = 1,2,…”), as shown by Shanbhag (1970).

AI 解读阅读原文 ↗BibTeX ↓