Second order asymptotics in nonlinear regression
TLDR
In this article, 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.About:
This article is published in Journal of Multivariate Analysis.The article was published on 1986-04-01 and is currently open access. It has received 28 citations till now. The article focuses on the topics: Estimator & Residual sum of squares.read more
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Asymptotic Theory of Nonlinear Least Squares Estimation
TL;DR: For a linear regression model, the necessary and sufficient condition for the asymptotic consistency of the least squares estimator is known as mentioned in this paper, and the condition is sufficient for the existence of any weakly consistent estimator, including the least square estimator.
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The Fitting of a Generalization of the Logistic Curve
TL;DR: In this article, the authors define a family of special cases of the Gompertz curve, including the exponential curve (6 → 0 through positive values), the logistic curve, and the exponential exponential curve with a fixed and a linear function of 0.
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Differential Geometry of Curved Exponential Families-Curvatures and Information Loss
TL;DR: The second-order information loss is calculated for Fisher-efficient estimators, and is decomposed into the sum of two non-negative terms: the exponential curvature of the estimator and the mixture curvature as mentioned in this paper.
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On the measurability and consistency of minimum contrast estimates
TL;DR: In this article, the existence of minimum contrast (m.c.) estimates for families of probability measures with upper semicontinuous densities has been shown to be a special case of the concept of maximum contrast.