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脆弱混合治愈模型中的惩罚似然变量选择AI 解读

Variable selection naturally arises as a useful subject when faced with data with massive predictor space. In addition to the massive dimensionality, the data may be characterized by intra-subject correlation, and cure fraction, which are ubiquitous in longitudinal studies with recurrent events defining the endpoint of interest. However, variable selection methods simultaneously adjusting for intra-subject correlation, and cure fraction are rare. We propose a comprehensive…

函数型混合专家模型AI 解读

We consider the statistical analysis of heterogeneous data for prediction in situations where the observations include functions, typically time series. We extend the modeling with Mixtures-of-Experts (ME), as a framework of choice in modeling heterogeneity in data for prediction with vectorial observations, to this functional data analysis context. We first present a new family of ME models, named functional ME (FME) in which the predictors are potentially noisy…

利用因子分析与低秩矩阵补全进行数据融合AI 解读

Data-fusion involves the integration of multiple related datasets. The statistical file-matching problem is a canonical data-fusion problem in multivariate analysis, where the objective is to characterise the joint distribution of a set of variables when only strict subsets of marginal distributions have been observed. Estimation of the covariance matrix of the full set of variables is challenging given the missing-data pattern. Factor analysis models use lower-dimensional…