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使用类别值函数型数据分析聚类社交媒体用户AI 解读

Social media provides more insight into consumer behavior than companies have ever had, and firms can interact with consumers on social media to increase their brand loyalty and address concerns they might have. However, it is critical for companies to evaluate whether the consumers they interact with have the potential to positively promote the firm. This can be challenging, especially when limited information is available about social media users. Our work proposes a…

个体化治疗方案的函数型特征构造AI 解读

Evidence-based personalized medicine formalizes treatment selection as an individualized treatment regime that maps up-to-date patient information into the space of possible treatments. Available patient information may include static features such race, gender, family history, genetic and genomic information, as well as longitudinal information including the emergence of comorbidities, waxing and waning of symptoms, side-effect burden, and adherence. Dynamic information…

基于软阈值高斯过程的标量对图像回归AI 解读

This work concerns spatial variable selection for scalar-on-image regression. We propose a new class of Bayesian nonparametric models and develop an efficient posterior computational aigorithm. The proposed soft-thresholded Gaussian process provides large prior support over the class of piecewise-smooth, sparse, and continuous spatially-varying regression coefficient functions. In addition, under some mild regularity conditions the soft-thresholded Gaussian proess prior…

广义多层函数型回归AI 解读

We introduce Generalized Multilevel Functional Linear Models (GMFLMs), a novel statistical framework for regression models where exposure has a multilevel functional structure. We show that GMFLMs are, in fact, generalized multilevel mixed models (GLMMs). Thus, GMFLMs can be analyzed using the mixed effects inferential machinery and can be generalized within a well researched statistical framework. We propose and compare two methods for inference: 1) a two-stage frequentist…