seff_init

<p>semiparametric efficient method initial value function</p>

internal

Utilize an orthogonality constrained optimization algorithm of
Wen & Yin (2013) <DOI:10.1007/s10107-012-0584-1> to solve a variety of
dimension reduction problems in the semiparametric framework, such as
Ma & Zhu (2012) <DOI:10.1080/01621459.2011.646925>, Ma & Zhu (2013)
<DOI:10.1214/12-AOS1072>, Sun, Zhu, Wang & Zeng (2019) <DOI:10.1093/biomet/asy064>
and Zhou, Zhu & Zeng (2021) <DOI:10.1093/biomet/asaa087>. The package also
implements some existing dimension reduction methods such as hMave by Xia, Zhang,
& Xu (2010) <DOI:10.1198/jasa.2009.tm09372> and partial SAVE by Feng, Wen & Zhu (2013)
<DOI:10.1080/01621459.2012.746065>. It also serves as a general purpose
optimization solver for problems with orthogonality constraints, i.e., in Stiefel
manifold. Parallel computing for approximating the gradient is enabled through 'OpenMP'.

seff_init: seff_init

Description

Usage

Arguments