A Hybrid Symbolic/Numeric Solution To Polynomial SEM

09/20/2021
by   Reinhard Oldenburg, et al.
0

There are many approaches to nonlinear SEM (structural equation modeling) but it seems that a rather straightforward approach using Isserlis' theorem has not yet been investigated although it allows the direct extension of the standard linear approach to nonlinear linear SEM. The reason may be that this method requires some symbolic calculations done at runtime. This paper describes the class of appropriate models and outlines the algorithm that calculates the covariance matrix and higher moments. Simulation studies show that the method works very well and especially that tricky models can be estimated accurately by taking higher movements into account, too.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

10/04/2021

Case based error variance corrected estimation of structural models

A new method for estimating structural equation models (SEM) is proposed...
10/15/2018

Population Symbolic Covariance Matrices for Interval Data

Symbolic Data Analysis (SDA) is a relatively new field of statistics tha...
05/21/2020

Graphical continuous Lyapunov models

The linear Lyapunov equation of a covariance matrix parametrizes the equ...
07/10/2019

Improved Structural Methods for Nonlinear Differential-Algebraic Equations via Combinatorial Relaxation

Differential-algebraic equations (DAEs) are widely used for modeling of ...
01/30/2019

Practicable Simulation-Free Model Order Reduction by Nonlinear Moment Matching

In this paper, a practicable simulation-free model order reduction metho...
03/03/2021

Modeling and control of 5-DoF boom crane

Automation of cranes can have a direct impact on the productivity of con...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.