Functional single index models for longitudinal data

By Ci-Ren Jiang, Jane-ling Wang. 2011

98 citations recorded by the source corpus.

A new single-index model that reflects the time-dynamic effects of the single index is proposed for longitudinal and functional response data, possibly measured with errors, for both longitudinal and time-invariant covariates. With appropriate initial estimates of the parametric index, the proposed estimator is shown to be √n-consistent and asymptotically normally distributed. We also address the nonparametric estimation of regression functions and provide estimates with optimal convergence rates. One advantage of the new approach is that the same bandwidth is used to estimate both the nonparametric mean function and the parameter in the index. The finite-sample performance for the proposed procedure is studied numerically.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/2fcf95413b10d9bc575a9c43bad86ffdaba8f05a

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Citations in the corpus

Functional Data Analysis (2016, background): “Jiang & Wang (2011) considered a setting where the link function may vary with time but the β in the index does not vary with time.”
Time-Varying Additive Models for Longitudinal Data (2013, background): “The time dynamic feature in the effects of longitudinal covariates was also considered in Jiang and Wang (2011) for a functional single index model.”