There are many active research projects accessing and applying shared ADNI data. Use the search above to find specific research focuses on the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | Lixia Hu |
| Institution: | Shanghai Lixin University of Accounting and Finance |
| Department: | 统计和数学学院 |
| Country: | |
| Proposed Analysis: | Zhang and Wang proposed a novel flexible nonparametric regression method, called varying-coefficient additive models (VCAMs) in the analysis of functional data.In this paper, we consider a mixed-effect semi-VCAM, which accommodates categorical covariates and accounts for the within subject correlation for sparse or dense longitudinal data. An iterative local linear smoothing method is proposed to estimate the varying-coefficient component functions and additive component functions, respectively. To avoid the subjective choice between sparse and dense case, we establish the asymptotic theorems of the newly-proposed estimators in a unified framework of sparse and dense longitudinal data. Furthermore, we construct consistent tests to justify whether an additive model or a varying coefficient model is enough for the real application at hand. Extensive simulation studies investigating the finite-sample performance of the proposed method further confirms our asymptotic results.We also illustrate our method via ANDI real-life examples http://ida.loni.ucla.edu/. |
| Additional Investigators |
