Kernel Mean Embedding of Probability Measures and its Applications to Functional Data Analysis Saeed Hayati Kenji Fukumizu
[email protected] [email protected] Afshin Parvardeh
[email protected] November 5, 2020 Abstract This study intends to introduce kernel mean embedding of probability measures over infinite-dimensional separable Hilbert spaces induced by functional response statistical models. The embedded function represents the concentration of probability measures in small open neighborhoods, which identifies a pseudo-likelihood and fosters a rich framework for sta- tistical inference. Utilizing Maximum Mean Discrepancy, we devise new tests in functional response models. The performance of new derived tests is evaluated against competitors in three major problems in functional data analysis including function-on-scalar regression, functional one-way ANOVA, and equality of covariance operators. 1 Introduction Functional response models are among the major problems in the context of Functional Data Analysis. A fundamental issue in dealing with functional response statistical models arises due to the lack of practical frameworks on characterizing probability measure on function spaces. This is mainly a con- sequence of the tremendous gap on how we present probability measures in arXiv:2011.02315v1 [math.ST] 4 Nov 2020 finite-dimensional and infinite-dimensional spaces. A useful property of finite-dimensional spaces is the existence of a locally fi- nite, strictly positive, and translation invariant measure like Lebesgue or count- ing measure, which makes it easy to take advantage of probability measures directly in the statistical inference. Fitting a statistical model, and estimat- ing parameters, hypothesis testing, deriving confidence regions and developing goodness of fit indices, all can be applied by integrating distribution or condi- tional distribution of response variables as a presumption into statistical proce- dures.