runvalid

Checks, if a given regression function is adequate with respect to the maximum run criterium.

regression

nonparametric

model

Implementation of the SSR-Algorithm. The Sign-Simplicity-Regression model is a nonparametric statistical model which is based on residual signs and simplicity assumptions on the regression function. Goal is to calculate the most parsimonious regression function satisfying the statistical adequacy requirements. Theory and functions are specified in Metzner (2020, ISBN: 979-8-68239-420-3, "Trendbasierte Prognostik") and Metzner (2021, ISBN: 979-8-59347-027-0, "Adäquates Maschinelles Lernen").

Lars Metzner

sisireg

Sign-Simplicity-Regression-Solver

runvalid function

<dl> <dt>dat</dt>
<dd>obervations.</dd> <dt>mu</dt>
<dd>discrete regression function.</dd> <dt>k</dt>
<dd>optional: maximum run length.</dd></dl>

Arguments

Author

Maximum Run Validity Check — runvalid

<dl>

 <dt>dat</dt>
<dd>obervations.</dd>

 <dt>mu</dt>
<dd>discrete regression function.</dd>

 <dt>k</dt>
<dd>optional: maximum run length.</dd>

</dl>

runvalid: Maximum Run Validity Check

Description

Usage

Value

Arguments

Author

References

Examples