Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

State Space Representation01:27

State Space Representation

The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
State Space to Transfer Function01:21

State Space to Transfer Function

The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
Transfer Function to State Space01:23

Transfer Function to State Space

State-space representation is a powerful tool for simulating physical systems on digital computers, necessitating the conversion of the transfer function into state-space form. Consider an nth-order linear differential equation with constant coefficients, like those encountered in an RLC circuit. The state variables are selected as the output and its n−1 derivatives. Differentiating these variables and substituting them back into the original equation produces the state equations.
In an RLC...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Linear time-invariant Systems01:23

Linear time-invariant Systems

A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be calculated...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

FABP4 as an immunometabolic hub in preeclampsia: from maternal-fetal interface to systemic inflammation.

Frontiers in immunology·2026
Same author

Co-infection with hepatitis B and human immunodeficiency virus: epidemiology, pathogenesis, and treatment.

Infectious diseases & immunity·2026
Same author

Synergistic dual ring-cleavage pathways enable efficient degradation of chlorobenzenes by Pseudomonas putida BS-1 in groundwater.

Journal of hazardous materials·2026
Same author

Spiritual leadership and service performance among Chinese flight attendants: The mediating effects of meaningful work and work engagement.

PloS one·2026
Same author

Efficacy and Safety of Firsekibart in Patients with Acute Gout Unsuitable for Standard Therapy: 48-Week Results from an Open-Label Extension of a Randomized Phase 3 Trial.

Advances in therapy·2026
Same author

Deciphering microenvironmental heterogeneity by scalable Niche Guided Module Discovery.

Communications biology·2026
Same journal

Planned missingness in intensive longitudinal studies: Extensions and comparisons of multiform designs.

Behavior research methods·2026
Same journal

A validity-guided workflow for robust large language model research in psychology.

Behavior research methods·2026
Same journal

Are 7-point Likert scales preferable to 5-point scales in language research?

Behavior research methods·2026
Same journal

Generative psychometrics via AI-GENIE: Automatic item generation and validation with network-integrated evaluation.

Behavior research methods·2026
Same journal

Exploring psychological tradeoffs: Developing and demonstrating an R Shiny app for Pareto optimization.

Behavior research methods·2026
Same journal

The performance of Bayesian fit measures in detecting misspecified multilevel structural equation modeling.

Behavior research methods·2026
See all related articles

Related Experiment Video

Updated: May 20, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

A SAS/IML program using the Kalman filter for estimating state space models.

Fei Gu1, Yiu-Fai Yung

  • 1Psychology and Research in Education, University of Kansas, Lawrence, KS, USA. fgu@ku.edu

Behavior Research Methods
|July 19, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a SAS/IML program for estimating parameters in linear Gaussian state space models (SSMs) using the Kalman filter. The open-source code aims to enhance the application and development of SSMs in research.

More Related Videos

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials
12:03

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

Published on: May 25, 2019

Interactive and Visualized Online Experimentation System for Engineering Education and Research
08:35

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Published on: November 24, 2021

Related Experiment Videos

Last Updated: May 20, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials
12:03

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

Published on: May 25, 2019

Interactive and Visualized Online Experimentation System for Engineering Education and Research
08:35

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Published on: November 24, 2021

Area of Science:

  • Statistics
  • Econometrics
  • Quantitative Psychology

Background:

  • State space models (SSMs) are powerful tools for analyzing time-series data.
  • Disseminating knowledge and software for SSMs is crucial for broader adoption.
  • Existing implementations may lack accessibility or flexibility for applied researchers.

Purpose of the Study:

  • To provide an accessible SAS/IML program for estimating parameters in general linear Gaussian SSMs.
  • To facilitate the use of the Kalman filter algorithm within the SAS environment.
  • To promote the advancement and application of SSMs as a research methodology.

Main Methods:

  • Development of a SAS/IML program implementing the Kalman filter algorithm.
  • Focus on general linear Gaussian state space models.
  • Utilizing the SAS/IML programming language for broad accessibility among SAS users.

Main Results:

  • A functional SAS/IML program for estimating SSM parameters is provided.
  • The program is designed for general linear Gaussian SSMs.
  • Open-source code allows for modification and improvement by users.

Conclusions:

  • The developed program enhances the practical application of SSMs.
  • It serves both applied researchers and quantitative methodologists.
  • Promotes further research and methodological development in state space modeling.