deriv_rw: Analytic D matrix Random Walk (RW) Process
In SMAC-Group/simts: Time Series Analysis Tools
deriv_rw R Documentation
Analytic D matrix Random Walk (RW) Process
Description
Obtain the first derivative of the Random Walk (RW) process.
Usage
deriv_rw(tau)
Arguments
tau
A vec containing the scales e.g. 2^{\tau}
Value
A matrix with the first column containing
the partial derivative with respect to \gamma^2.
Process Haar WV First Derivative
Taking the derivative with respect to \gamma ^2 yields:
\frac{\partial }{{\partial {\gamma ^2}}}\nu _j^2\left( {{\gamma ^2}} \right) = \frac{{\tau _j^2 + 2}}{{12{\tau _j}}}
Author(s)
James Joseph Balamuta (JJB)
SMAC-Group/simts documentation built on Sept. 4, 2023, 5:25 a.m.
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| deriv_rw | R Documentation |
Analytic D matrix Random Walk (RW) Process
Description
Obtain the first derivative of the Random Walk (RW) process.
Usage
deriv_rw(tau)
Arguments
tau |
A |
Value
A matrix with the first column containing
the partial derivative with respect to \gamma^2.
Process Haar WV First Derivative
Taking the derivative with respect to \gamma ^2 yields:
\frac{\partial }{{\partial {\gamma ^2}}}\nu _j^2\left( {{\gamma ^2}} \right) = \frac{{\tau _j^2 + 2}}{{12{\tau _j}}}
Author(s)
James Joseph Balamuta (JJB)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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