Parameter Estimation of a Partially Observed Hypoelliptic Stochastic Linear System
In this article, we address the problem of the parameter estimation of a partially observed linear hypoelliptic stochastic system in continuous time, a relevant problem in various fields, including mechanical and structural engineering. We propose an online approach which is an approximation to the...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-02-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/13/3/529 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849717718958211072 |
|---|---|
| author | Nilton O. B. Ávido Paula Milheiro-Oliveira |
| author_facet | Nilton O. B. Ávido Paula Milheiro-Oliveira |
| author_sort | Nilton O. B. Ávido |
| collection | DOAJ |
| description | In this article, we address the problem of the parameter estimation of a partially observed linear hypoelliptic stochastic system in continuous time, a relevant problem in various fields, including mechanical and structural engineering. We propose an online approach which is an approximation to the expectation–maximization (EM) algorithm. This approach combines the Kalman–Bucy filter, to deal with partial observations, with the maximum likelihood estimator for a degenerate <i>n</i>-dimensional system under complete observation. The performance of the proposed approach is illustrated by means of a simulation study undertaken on a harmonic oscillator that describes the dynamic behavior of an elementary engineering structure subject to random vibrations. The unknown parameters represent the oscillator’s stiffness and damping coefficients. The simulation results indicate that, as the variance of the observation error vanishes, the proposed approach remains reasonably close to the output of the EM algorithm, with the advantage of a significant reduction in computing time. |
| format | Article |
| id | doaj-art-4b9d1b01a03a40d98d419ccad8fc9d5f |
| institution | DOAJ |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-4b9d1b01a03a40d98d419ccad8fc9d5f2025-08-20T03:12:35ZengMDPI AGMathematics2227-73902025-02-0113352910.3390/math13030529Parameter Estimation of a Partially Observed Hypoelliptic Stochastic Linear SystemNilton O. B. Ávido0Paula Milheiro-Oliveira1Polytechnic Institute of Huila, Mandume Ya Ndemufayo University, Arimba Main Road, 776, Lubango P.O. Box 201, AngolaCenter for Mathematics of the University of Porto (CMUP), Rua do Campo Alegre s/n, 4169-007 Porto, PortugalIn this article, we address the problem of the parameter estimation of a partially observed linear hypoelliptic stochastic system in continuous time, a relevant problem in various fields, including mechanical and structural engineering. We propose an online approach which is an approximation to the expectation–maximization (EM) algorithm. This approach combines the Kalman–Bucy filter, to deal with partial observations, with the maximum likelihood estimator for a degenerate <i>n</i>-dimensional system under complete observation. The performance of the proposed approach is illustrated by means of a simulation study undertaken on a harmonic oscillator that describes the dynamic behavior of an elementary engineering structure subject to random vibrations. The unknown parameters represent the oscillator’s stiffness and damping coefficients. The simulation results indicate that, as the variance of the observation error vanishes, the proposed approach remains reasonably close to the output of the EM algorithm, with the advantage of a significant reduction in computing time.https://www.mdpi.com/2227-7390/13/3/529EM algorithmpartially observed systemsparameter estimationstochastic differential equationshypoelliptic modelsharmonic oscillator |
| spellingShingle | Nilton O. B. Ávido Paula Milheiro-Oliveira Parameter Estimation of a Partially Observed Hypoelliptic Stochastic Linear System Mathematics EM algorithm partially observed systems parameter estimation stochastic differential equations hypoelliptic models harmonic oscillator |
| title | Parameter Estimation of a Partially Observed Hypoelliptic Stochastic Linear System |
| title_full | Parameter Estimation of a Partially Observed Hypoelliptic Stochastic Linear System |
| title_fullStr | Parameter Estimation of a Partially Observed Hypoelliptic Stochastic Linear System |
| title_full_unstemmed | Parameter Estimation of a Partially Observed Hypoelliptic Stochastic Linear System |
| title_short | Parameter Estimation of a Partially Observed Hypoelliptic Stochastic Linear System |
| title_sort | parameter estimation of a partially observed hypoelliptic stochastic linear system |
| topic | EM algorithm partially observed systems parameter estimation stochastic differential equations hypoelliptic models harmonic oscillator |
| url | https://www.mdpi.com/2227-7390/13/3/529 |
| work_keys_str_mv | AT niltonobavido parameterestimationofapartiallyobservedhypoellipticstochasticlinearsystem AT paulamilheirooliveira parameterestimationofapartiallyobservedhypoellipticstochasticlinearsystem |