Wavelet-Based Scalar-on-Function Finite Mixture Regression Models.
Wavelet-Based Scalar-on-Function Finite Mixture Regression Models.
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DOI：
10.1016/j.csda.2014.11.017
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发表时间：
2016-01-01
影响因子：
1.8
通讯作者：
Ogden RT
中科院分区：
文献类型：
--
作者：
Ciarleglio A;Ogden RT
经典有限混合回归可用于对标量预测因子和标量响应之间的关系进行建模，所述标量响应来自由这些预测因子和响应之间的不同关联定义的亚群。经典的有限混合回归模型进行了扩展，将功能预测，通过采取基于小波的方法，其中功能预测和组件的特定系数函数表示在一个适当的小波基。通过使用模型的小波表示，对应于函数协变量的系数成为预测因子。在这种情况下，预测因子通常比观测值多得多。因此，套索型惩罚，同时进行特征选择和估计。讨论了该模型的规格，并给出了拟合算法。基于小波的方法在合成数据上进行评估，并应用于多发性硬化受试者认知能力和离散张量成像测量之间关系研究的真实的数据集。1
Classical finite mixture regression is useful for modeling the relationship between scalar predictors and scalar responses arising from subpopulations defined by the di ering associations between those predictors and responses. The classical finite mixture regression model is extended to incorporate functional predictors by taking a wavelet-based approach in which both the functional predictors and the component-specific coefficient functions are represented in terms of an appropriate wavelet basis. By using the wavelet representation of the model, the coefficients corresponding to the functional covariates become the predictors. In this setting, there are typically many more predictors than observations. Hence a lasso-type penalization is employed to simultaneously perform feature selection and estimation. Specification of the model is discussed and a fitting algorithm is provided. The wavelet-based approach is evaluated on synthetic data as well as applied to a real data set from a study of the relationship between cognitive ability and di usion tensor imaging measures in subjects with multiple sclerosis.1