The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Computational aspects of maximum likelihood estimation and reduction in sensitivity function calculations
Abstract
This paper discusses numerical aspects of computing maximum likelihood estimates for linear dynamical systems in state-vector form. Different gradient-based nonlinear programming methods are discussed in a unified framework and their applicability to maximum likelihood estimation is examined. The problems due to singular Hessian or singular information matrix that are common in practice are discussed in detail and methods for their solution are proposed. New results on the calculation of state sensitivity functions via reduced order models are given. Several methods for speeding convergence and reducing computation time are also discussed.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
An Algorithm for Least-Squares Estimation of Nonlinear Parameters
Marquardt, D. W. (1963) · Journal of the Society for Industrial and Applied Mathematics · doi:10.1137/0111030 · Cited by 30,728
Cited by
Kitanidis, P. K., & Vomvoris, E. G. (1983) · Water Resources Research · doi:10.1029/wr019i003p00677 · Cited by 536
The Signal Extraction Approach to Nonlinear Regression and Spline Smoothing
Wecker, W. E., & Ansley, C. F. (1983) · Journal of the American Statistical Association · doi:10.1080/01621459.1983.10477935 · Cited by 295
Unbiased estimation of mutation rates under fluctuating final counts.
Ycart, B., & Veziris, N. (2014) · PLoS ONE · doi:10.1371/journal.pone.0101434 · Cited by 19
Protein Folding Is Mechanistically Robust
Weber, J. K., & Pande, V. S. (2012) · Biophysical Journal · doi:10.1016/j.bpj.2012.01.028 · Cited by 13
Metadata
- Authors
- Gupta, N., & Mehra, R.
- Venue
- IEEE Transactions on Automatic Control
- Published
- 1974-12
- Volume
- 19
- Issue
- 6
- Pages
- 774-783
- ISSN
- 0018-9286 (ISSN-L)
- OpenAlex
- W2082068808
- Cited by
- 264
- References
- 1
- FWCI
- 15.92
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Computational aspects of maximum likelihood estimation and reduction in sensitivity function calculations. https://app.cochat.ai/papers/10.1109/tac.1974.1100714
- The paper
- Gupta, N., & Mehra, R. (1974). Computational aspects of maximum likelihood estimation and reduction in sensitivity function calculations. IEEE Transactions on Automatic Control. https://doi.org/10.1109/tac.1974.1100714
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
