In disease surveillance applications, the disease events are modeled by spatial-temporal point processes. We propose a new class of semi-parametric generalized linear mixed Cox model for such data, where the event rate is related to some known risk factors and some unknown latent random effects. We model the latent spatial-temporal process as spatially correlated functional data, and propose co...

Fundamental frequency (F0, broadly “pitch”) is an integral part of spoken human language; however, a comprehensive quantitative model for F0 can be a challenge to formulate due to the large number of effects and interactions between effects that lie behind the human voice’s production of F0, and the very nature of the data being a contour rather than a point. This paper presents a semi-parametr...

This paper proposes a new framework to analyze the nonstationarity in the time series of state densities, representing either cross-sectional or intra-period distributions of some underlying economic variables. We regard each state density as a realization of Hilbertian random variable, and use a functional time series model to fit a given time series of state densities. This allows us to explo...

This paper proposes a new framework to analyze the nonstationarity in the time series of state densities, representing either cross-sectional or intra-period distributions of some underlying economic variables. We regard each state density as a realization of Hilbertian random variable, and use a functional time series model to fit a given time series of state densities. This allows us to explo...

We explore a connection between the singular value decomposition (SVD) and functional principal component analysis (FPCA) models in high-dimensional brain imaging applications. We formally link right singular vectors to principal scores of FPCA. This, combined with the fact that left singular vectors estimate principal components, allows us to deploy the numerical efficiency of SVD to fully est...

Re-sampling methods for estimating the distribution of descriptive statistics of functional data are considered. Through Monte-Carlo simulations, we compare the performance of several re-sampling methods commonly used for estimating the distribution of descriptive statistics. We introduce two re-sampling methods that rely on functional principal component analysis, where the scores were randoml...