Abstract
We show that the techniques developed by Pázman (1984, 1992, 1993) and Pázman and Pronzato (1992, 1996) for the computation of approximate densities of LS estimators in nonlinear regression can be extended to investigate two bias-corrected LS estimators: that suggested by Firth (1993) and the two-stage LS estimator proposed by the authors (Pronzato and Pázman, 1994). This, together with the possibility to consider weighted LS estimators with arbitrary weights (Pázman, 1993, chap. 7.4), or ML estimators in more general models (Pázman, 1993, chap. 9.4), shows that these techniques can be used far beyond the ordinary LS estimator. Numerical examples are presented in Pázman and Pronzato (1997).
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Pázman, A., Pronzato, L. (1998). Approximate Densities of Two Bias-Corrected Nonlinear LS Estimators. In: Atkinson, A.C., Pronzato, L., Wynn, H.P. (eds) MODA 5 — Advances in Model-Oriented Data Analysis and Experimental Design. Contributions to Statistics. Physica, Heidelberg. https://doi.org/10.1007/978-3-642-58988-1_16
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DOI: https://doi.org/10.1007/978-3-642-58988-1_16
Publisher Name: Physica, Heidelberg
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