Co.LPM_nD.batch

Internal batched backend for evaluating <code>Co.LPM_nD</code> over many targets.

internal

NNS (Nonlinear Nonparametric Statistics) leverages partial moments – the fundamental elements of variance that asymptotically approximate the area under f(x) – to provide a robust foundation for nonlinear analysis while maintaining linear equivalences. Designed for real-world data that violates symmetry, linearity, or distributional assumptions, NNS delivers a comprehensive suite of advanced statistical techniques, including: Numerical integration, Numerical differentiation, Clustering, Correlation, Dependence, Causal analysis, ANOVA, Regression, Classification, Seasonality, Autoregressive modeling, Normalization, Stochastic superiority / dominance and Advanced Monte Carlo sampling. All routines based on: Viole, F. and Nawrocki, D. (2013), Nonlinear Nonparametric Statistics: Using Partial Moments (ISBN: 1490523995, Second edition: <https://ovvo-financial.github.io/NNS/book/>).

Fred Viole

Nonlinear Nonparametric Statistics

Roberto Spadim

Rasheed Khoshnaw

Co.LPM_nD.batch function

<dl><dt>data</dt>
<dd>A numeric matrix with observations in rows and variables in columns.</dd>
<dt>targets</dt>
<dd>A numeric matrix with target rows and the same number of columns as data.</dd>
<dt>degree</dt>
<dd>numeric; degree for lower deviations.</dd>
<dt>norm</dt>
<dd>logical; normalize result.</dd></dl>

Arguments

Batched Co-Lower Partial Moment nD — Co.LPM_nD.batch

<dl>

<dt>data</dt>
<dd>A numeric matrix with observations in rows and variables in columns.</dd>


<dt>targets</dt>
<dd>A numeric matrix with target rows and the same number of columns as data.</dd>


<dt>degree</dt>
<dd>numeric; degree for lower deviations.</dd>


<dt>norm</dt>
<dd>logical; normalize result.</dd>

</dl>

Co.LPM_nD.batch: Batched Co-Lower Partial Moment nD

Description

Usage

Value

Arguments