Second order asymptotics in nonlinear regression

Wolfgang H. Schmidt and S. Zwanzig

Journal of Multivariate Analysis, 1986, vol. 18, issue 2, 187-215

Abstract: It is a well known part of statistical knowledge that first order asymptotically efficient procedures can be misleading for moderate sample sizes. Usually this is demonstrated for some popular special cases including numerical comparisons. Typically the situation is worse if nuisance parameters are present. In this paper we give second order asymptotically efficient tests, confidence regions, and estimators for the nonlinear regression model which are based on the least-squares estimator and the residual sum of squares.

Keywords: Nonlinear; regression; Edgeworth; expansion; second; order; asymptotics; hypothesis; testing; median; unbiased; estimators; confidence; regions (search for similar items in EconPapers)
Date: 1986
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