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Distance Association Models for the Analysis of Repeated Transition Frequency Tables

机译:重复转换频率表分析的距离关联模型

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The present paper is concerned with the analysis of repeated transition frequency tables, for example, occupational mobility data measured in different cohorts. The association present in such a table will be modeled by a distance in Euclidean space. A large distance corresponds to a small association; a small distance corresponds to a large association. A more direct interpretation is that more transitions occur between categories that are close together in a social space. It is assumed that the same social structure (space) exists for the different slices (cohorts/time points) of a table, but that the dimensions of this space are weighted for the different slices, i.e., for each slice the dimensions are stretched or squeezed. We will propose a model, discuss an algorithm to obtain maximum likelihood estimates and apply the model to an empirical data set.
机译:本文关注于重复的过渡频率表的分析,例如,在不同队列中测量的职业流动性数据。此类表中存在的关联将通过欧氏空间中的距离进行建模。距离越大,关联就越小;较小的距离对应于较大的关联。更直接的解释是,在社交空间中彼此靠近的类别之间会发生更多的转换。假设表的不同部分(群组/时间点)存在相同的社会结构(空间),但是针对不同部分对该空间的维度加权,即,对于每个部分,维度被拉伸或挤压。我们将提出一个模型,讨论一种获得最大似然估计的算法,并将该模型应用于经验数据集。

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