TOSI: Two-Directional Simultaneous Inference for High-Dimensional
A general framework of two directional simultaneous inference
is provided for high-dimensional as well as the fixed dimensional models with manifest
variable or latent variable structure, such as high-dimensional mean models, high-
dimensional sparse regression models, and high-dimensional latent factors models.
It is making the simultaneous inference on a set of parameters from two directions,
one is testing whether the estimated zero parameters indeed are zero and the other is
testing whether there exists zero in the parameter set of non-zero. More details can be
referred to Wei Liu, et al. (2022) <doi:10.48550/arXiv.2012.11100>.
Please use the canonical form
to link to this page.