Abstract
In this paper, exponential type regression models are considered from geometric point of view. The stochastic expansions relating to the estimate are derived and are used to study several asymptotic inference problems. The biases and the covariances relating to the estimate may be calculated based on the expansions. The information loss of the estimate and a limit theorem connected with the observed and expected Fisher informations are obtained in terms of the curvatures.
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The project was supported by National Natural Science Foundation of China
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Bocheng, W., Yangming, M. Geometry of exponential type regression models and its asymptotic inference. Appl. Math. 8, 182–197 (1993). https://doi.org/10.1007/BF02662002
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DOI: https://doi.org/10.1007/BF02662002