Likelihood Based Inference in Non‐linear Regression Models Using the p* and R* Approach

A. S. Tocquet

Scandinavian Journal of Statistics, 2001, vol. 28, issue 3, 429-443

Abstract: We develop second order asymptotic results for likelihood‐based inference in Gaussian non‐linear regression models. We provide an approximation to the conditional density of the maximum likelihood estimator given an approximate ancillary statistic (the affine ancillary). From this approximation, we derive a statistic to test an hypothesis on one component of the parameter. This test statistic is an adjustment of the signed log‐likelihood ratio statistic. The distributional approximations (for the maximum likelihood estimator and for the test statistic) are of second order in large deviation regions.

Date: 2001
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https://doi.org/10.1111/1467-9469.00246

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Persistent link: https://EconPapers.repec.org/RePEc:bla:scjsta:v:28:y:2001:i:3:p:429-443

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