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作者：JONES, MC; LINTON, O; NIELSEN, JP
作者单位：Yale University
摘要：A new method for bias reduction in nonparametric density estimation is proposed. The method is a simple, two-stage multiplicative bias correction. Its theoretical properties are investigated, and simulations indicate its practical potential. The method is easy to compute and to analyse, and extends simply to multivariate and other estimation problems.
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作者：GLAD, IK; SEBASTIANI, G
摘要：Synthetic magnetic resonance imaging involves the estimation, based on a set of measured images with noise, of three basic physical quantities that are nonlinearly related to the observations. The methods currently available for this ill-conditoned inverse problem either do not provide sufficiently accurate estimates or require time-consuming data collection. We formulate this nonlinear problem in a Bayesian framework, taking into account knowledge about the physics of the magnetic resonance i...
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作者：HJELLVIK, V; TJOSTHEIM, D
摘要：We introduce tests of linearity for time series based on nonparametric estimates of the conditional mean and the conditional variance. The tests are compared to a number of parametric tests and to nonparametric tests based on the bispectrum. Asymptotic expressions give bad approximations, and the null distribution under linearity is constructed using resampling of the best linear approximation. The new tests perform well on the examples tested.
