We present a pipeline for a statistical textual exploration, offering a
stylometry-based explanation and statistical validation of a hypothesized
partition of a text. Given a parameterization of the text, our pipeline: (1)
detects literary features yielding the optimal overlap between the hypothesized
and unsupervised partitions, (2) performs a hypothesis-testing analysis to
quantify the statistical significance of the optimal overlap, while conserving
implicit correlations between units of text that are more likely to be grouped,
and (3) extracts and quantifies the importance of features most responsible for
the classification, estimates their statistical stability and cluster-wise
abundance.
We apply our pipeline to the first two books in the Bible, where one
stylistic component stands out in the eyes of biblical scholars, namely, the
Priestly component. We identify and explore statistically significant stylistic
differences between the Priestly and non-Priestly components.

本文提出了一种用于文本探索的统计学流程，通过基于风格学的解释和统计验证，假设了文本的划分，探测文本的文学特点并量化文本特征以确定是否符合假设的划分。研究者将此流程应用于圣经前两卷书，并发现圣职者成分与非圣职者成分之间存在显著的文体差异。

文本分段成分的统计探索：以《创世记》和《出埃及记》中祭司来源为例

A Statistical Exploration of Text Partition Into Constituents: The Case  of the Priestly Source in the Books of Genesis and Exodus

Recently the interest of researchers has shifted from the analysis of
synchronous relationships of financial instruments to the analysis of more
meaningful asynchronous relationships. Both of those analyses are concentrated
only on Pearson's correlation coefficient and thus intraday lead-lag
relationships associated with such. Under Efficient Market Hypothesis such
relationships are not possible as all information is embedded in the prices. In
this paper we analyse lead-lag relationships of financial instruments and
extend known methodology by using mutual information instead of Pearson's
correlation coefficient, which not only is a more general measure, sensitive to
non-linear dependencies, but also can lead to a simpler procedure of
statistical validation of links between financial instruments. We analyse
lagged relationships using NYSE 100 data not only on intraday level but also
for daily stock returns, which has usually been ignored.

本文分析了金融工具之间的先导关系，扩展了已知方法，使用了互信息作为更一般的指标，敏感于非线性依赖性，可以导致更简单的统计验证过程，同时分析了 NYSE 100 数据中的滞后关系，包括日内水平和日常股票收益。