Abstract
A general expository description is given of the use of quadratic score test statistics as inference functions. This methodology allows one to do efficient estimation and testing in a semiparametric model defined by a set of mean-zero estimating functions. The inference function is related to a quadratic minimum distance problem. The asymptotic chi-squared properties are shown to be the consequences of asymptotic projection properties. Shortcomings of the asymptotic theory are discussed and a bootstrap method is shown to correct for anticonservative testing behavior.
Original language | English (US) |
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Pages (from-to) | 394-410 |
Number of pages | 17 |
Journal | Statistical Science |
Volume | 18 |
Issue number | 3 |
DOIs | |
State | Published - Aug 2003 |
Externally published | Yes |
Keywords
- Bootstrapping
- Chi-squared test
- Edgeworth expansion
- Generalized estimating equation
- Generalized method of moments
- Likelihood
- Quadratic inference function
- Quasi-likelihood
- Semiparametric model
ASJC Scopus subject areas
- Statistics and Probability
- Mathematics(all)
- Statistics, Probability and Uncertainty