Abstract
This paper looks at boundary effects on inference in an important class of models including, notably, logistic regression. Asymptotic results are not uniform across such models. Accordingly, whatever their order, methods asymptotic in sample size will ultimately ’break down’ as the boundary is approached, in the sense that effects
such as infinite skewness, discreteness and collinearity will dominate. In this paper, a highly interpretable diagnostic tool is proposed allowing the analyst to check if the boundary is going to have an appreciable effect on standard inferential techniques.
such as infinite skewness, discreteness and collinearity will dominate. In this paper, a highly interpretable diagnostic tool is proposed allowing the analyst to check if the boundary is going to have an appreciable effect on standard inferential techniques.
| Original language | English |
|---|---|
| Pages (from-to) | 17-22 |
| Number of pages | 6 |
| Journal | Stat |
| Volume | 3 |
| Issue number | 1 |
| Early online date | 21 Feb 2014 |
| DOIs | |
| Publication status | Published - Feb 2014 |
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Anaya Izquierdo, K. A., Critchley, F., & Marriott, P. (2014). When are first order asymptotics adequate? A diagnostic. Stat, 3(1), 17-22. https://doi.org/10.1002/sta4.40