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原文連結
論文資訊
- 類型：已發表論文
- 日期：2020-05-25
摘要
The analysis of questionnaires often involves representing the high-dimensional responses in a low-dimensional space (e.g., PCA, MCA, or t-SNE). However questionnaire data often contains categorical variables and common 統計 model assumptions rarely hold. Here we present a non-parametric approach based on Fisher 資訊 which obtains a low-dimensional embedding of a 統計 manifold (SM). The SM has deep connections with parametric 統計 models and the theory of 相變s in 統計 physics. Firstly we simulate questionnaire responses based on a non-linear SM and validate our method compared to other methods. Secondly we apply our method to two empirical datasets containing largely categorical variables: an anthropological survey of rice farmers in Bali and a cohort study on health 不平等 in Amsterdam. Compare to prev
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