fdaconcur {fdaconcur}

fdaconcur: Concurrent Regression and History Index Models for Functional Data

Description

This package provides tools for functional concurrent regression and history index models.

Details

fdaconcur for Functional Concurrent Regression and History Index Models

Provides an implementation of concurrent or varying coefficient regression methods for functional data. The implementations are done for both dense and sparsely observed functional data. Pointwise confidence bands can be constructed for each case. Further, the influence of past predictor values are modeled by a smooth history index function, while the effects on the response are described by smooth varying coefficient functions, which are very useful in analyzing real data such as COVID data.

References: Yao, F., Müller, H.G., Wang, J.L. (2005) <doi: 10.1214/009053605000000660>. Sentürk, D., Müller, H.G. (2010) <doi: 10.1198/jasa.2010.tm09228>.

PACE is based on the idea that observed functional data are generated by a sample of underlying (but usually not fully observed) random trajectories that are realizations of a stochastic process. It does not rely on pre-smoothing of trajectories, which is problematic if functional data are sparsely sampled.

Maintainer: Su I Iao [e-mail]

Author(s)

Satarupa Bhattacharjee Yaqing Chen Changbo Zhu Han Chen Yidong Zhou Álvaro Gajardo Poorbita Kundu Hang Zhou

Hans-Georg Müller [e-mail]

See Also

Useful links:

• https://github.com/functionaldata/tFDAconcur

• Report bugs at https://github.com/functionaldata/tFDAconcur/issues