Thursday, Aug 7: 8:30 AM - 10:20 AM
0454
Invited Paper Session
Music City Center
Room: CC-102B
Applied
Yes
Main Sponsor
Section on Nonparametric Statistics
Co Sponsors
Biometrics Section
Mental Health Statistics Section
Presentations
The COVID-19 pandemic has precipitated a significant mental health crisis, notably impacting young adults. With the rapid advancement of technology and the ubiquitous use of social media among this demographic, there has been a marked increase in the expression of emotions and mental health concerns on these platforms. We propose methods to study trends in mental health issues, self-harm, and violence towards others as conveyed through social media posts. Traditional methods of analyzing such data often lack interpretability, high computational demands, or inflexibility in data handling. In this work, we develop a flexible statistical framework designed to analyze high-resolution data from social media, aiming to identify users exhibiting atypical posting behaviors.
Functional principal component analysis (FPCA) is a critical technique for dimension reduction in functional data analysis (FDA). Traditional FPCA methods assume a linear structure in the observed functional data, which may not always hold, leading to inefficiencies when the data exhibits nonlinear characteristics. In this study, we propose a novel FPCA method that accommodates nonlinear structures using neural networks. We design networks specifically for functional data and explore their universal approximation properties. We conduct a simulation study to evaluate the performance of our method and apply it to a real-world dataset to further demonstrate its effectiveness. This talk is based on joint work with Rou Zhong and Jingxiao Zhang.
Keywords
Curve reconstruction
Nonlinear dimension reduction
Unsupervised learning
In this talk we present a new method of linear regression using Hilbert-space valued covariates with unknown reproducing kernels. We develop a computationally efficient approach to estimation and derive asymptotic theory for the regression parameter estimates under mild assumptions. We demonstrate the approach in simulation studies as well as in a data analyses using two- and three-dimensional brain images as predictors.
In studies of chronic diseases, the health status of a subject can often be characterized by a finite number of transient disease states and an absorbing state, such as death. The times of transitions among the transient states are ascertained through periodic examinations and thus interval-censored. The time of reaching the absorbing state is known or right-censored, with the transient state at the previous instant being unobserved. We provide a general framework for analyzing such multi-state data. We formulate the effects of potentially time-dependent covariates on
the multi-state disease process through semiparametric proportional intensity models with random effects. We combine nonparametric maximum likelihood estimation with sieve estimation and develop a stable expectation-maximization algorithm. We establish the asymptotic properties of the proposed estimators and assess the performance of the proposed methods through extensive simulation studies. Finally, we provide an illustration with
a cardiac allograft vasculopathy study.
Keywords
Multi-state model
Interval censoring
Nonparametric maximum likelihood estimation
Semiparametric efficiency
EM algorithm