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- W2078373380 abstract "Following HAJEK [4] and BERAN [3], we define local asymptotic minimaxity of estimators for certain magnitudes in inadequate nonlinear regression and derive optimality for special cases. The first component of the parameter yielding the best approximation of the vector of expectations, the mean squared error of prediction, the sum of squared model error and error variance, the differences between squared model errors and between mean squared errors of estimation related to two given models are under consideration. In sectin 4 we introduce a concept of local asymptotic efficiency of test procedures of sponding to the same situations and obtain efficient tests. In the whole paper normality of the error terms is assumed" @default.
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- W2078373380 date "1988-01-01" @default.
- W2078373380 modified "2023-09-25" @default.
- W2078373380 title "Asymptotic optimality of estimators and tests in inadequate nonlinear regression" @default.
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- W2078373380 doi "https://doi.org/10.1080/02331888808802085" @default.
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