Summary of Particle Swarm Optimization with Applications to Maximum Likelihood Estimation and Penalized Negative Binomial Regression, by Sisi Shao et al.
Particle swarm optimization with Applications to Maximum Likelihood Estimation and Penalized Negative Binomial Regression
by Sisi Shao, Junhyung Park, Weng Kee Wong
First submitted to arxiv on: 20 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Applications (stat.AP); Computation (stat.CO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Particle Swarm Optimization (PSO) algorithm is a general-purpose optimization routine that can be used to estimate model parameters in nonstandard distributions. Compared to existing algorithms like nlminb, optim, and nlmixed, PSO shows promise in reproducing similar results while also producing more optimal solutions when others fail to converge. The advantages of using PSO are highlighted through four examples, including the identification of unidentified parameters in a generalized distribution, estimation results for log-binomial regressions, flexibility in link functions for binomial regression with LASSO penalty, and superior MLE estimates for an EE-IW distribution. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores a new way to optimize model parameters using Particle Swarm Optimization (PSO). PSO is compared to other common optimization routines like nlminb, optim, and nlmixed. The results show that PSO can do the same things as these other methods, but sometimes it does them better. This is especially true when the problem is hard or tricky. The authors use four examples to show how PSO works well in different situations. |
Keywords
» Artificial intelligence » Optimization » Regression
