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Abstract. The problem of estimating an unknown density function has been widely studied. In this article, we present a convolution estimator for the density of the responses in a nonlinear heterogenous regression model. The rate of convergence for the mean square error of the convolution estimator is of order n ?1 under certain regularity conditions. This is faster than the rate for the kernel density method. We derive explicit expressions for the asymptotic variance and the bias of the new estimator, and further a data‐driven bandwidth selector is proposed. We conduct simulation experiments to check the finite sample properties, and the convolution estimator performs substantially better than the kernel density estimator for well‐behaved noise densities. 相似文献
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A critical survey on the status of multiple criteria decision making theory and practice 总被引:6,自引:0,他引:6
TJ Stewart 《Omega》1992,20(5-6)
This paper seeks to review and to contrast the main streams of thought in Multiple Criteria Decision Making (MCDM) theory and practice, without attempting to review all MCDM methods in detail. The main purpose is to identify pitfalls in the usage of various approaches, and to suggest approaches which are most robustly and effectively useable, especially by the non-expert in MCDM methodology. Problem areas in MCDM still requiring further research are also discussed. 相似文献
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