ccorFun

Evaluate the smallest canonical correlation for two set of variables, each set of variables is represented by a matrix whose columns are variables.

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>.

Wei Liu

TOSI

Two-Directional Simultaneous Inference for High-Dimensional
Models

Huazhen Lin

ccorFun function

<dl> <dt>hH</dt>
<dd>a <code>n</code>-by-<code>q</code> matrix, one set of q variables.</dd> <dt>H</dt>
<dd>a <code>n</code>-by-<code>q</code> matrix, the other set of q variables.</dd></dl>

Arguments

Author

Evaluate the smallest canonical correlation for two set of variables — ccorFun

<dl>

 <dt>hH</dt>
<dd>a <code>n</code>-by-<code>q</code> matrix, one set of q variables.</dd>

 <dt>H</dt>
<dd>a <code>n</code>-by-<code>q</code> matrix, the other set of q variables.</dd>

</dl>

ccorFun: Evaluate the smallest canonical correlation for two set of variables

Description

Usage

Value

Arguments

Author

See Also

Examples