Local solutions in the estimation of growth mixture models.
Bibliographic Data
| ID | 23366451 |
|---|---|
| Authors | J R Hipp (0000-0001-9006-2587, University of North Carolina at Chapel Hill), Daniel J Bauer (0000-0001-6425-3769, University of North Carolina at Chapel Hill) |
| Year | 2006 |
| Volume | 11 |
| Issue | 1 |
| Pages | 36-53 |
| Publication date | 2006-01-01 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Psychological Methods (JOURNAL) |
| Journal identifiers | ISSN: 1082-989X • E-ISSN: 1939-1463 |
| Publisher | American Psychological Association (APA) (PUBLISHER) |
| DOI | 10.1037/1082-989x.11.1.36 |
| PMID | 16594766 |
| OpenAlex | W2114909733 |
| Language | EN |
| Citations received | 264 |
| References cited | 32 |
Finite mixture models are well known to have poorly behaved likelihood functions featuring singularities and multiple optima. Growth mixture models may suffer from fewer of these problems, potentially benefiting from the structure imposed on the estimated class means and covariances by the specified growth model. As demonstrated here, however, local solutions may still be problematic. Results from an empirical case study and a small Monte Carlo simulation show that failure to thoroughly consider the possible presence of local optima in the estimation of a growth mixture model can sometimes have serious consequences, possibly leading to adoption of an inferior solution that differs in substantively important ways from the actual maximum likelihood solution. Often, the defaults of current software need to be overridden to thoroughly evaluate the parameter space and obtain confidence that the maximum likelihood solution has in fact been obtained.
Econometrics · Economics · Estimation · Estimation theory · Local optimum · Mathematical optimization · Maximum likelihood · Mixture model · Monte Carlo method · Statistics · Applied Mathematics · Bayesian Methods and Mixture Models · Computer Science · Fuzzy Systems and Optimization · Mathematics · Statistical Methods and Bayesian Inference
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| Unique citing works | 264 |
|---|---|
| Citations per year | 13,2 |
| Citation span | 2006 - 2026 (21) |
| Citation velocity | current |
| Highly cited | Yes |
| Citation types | Neutral: 240 |