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删失数据线性回归模型的精确近似条件推断与贝叶斯推断方法AI 解读

A method to approximate marginal posterior distribution functions of regression coefficients and scale parameters is presented. It is based on a general asymptotic formula of DiCiccio, Field & Fraser (1990) for approximating marginal tail probabilities. In developing the method, it is shown that the accuracy of the approximation of DiCiccio et al. can be substantially increased in certain cases by using a suitable change of variables, and a general approach to deriving such…

删失数据线性回归的精确近似条件推断与贝叶斯推断AI 解读

A method to approximate marginal posterior distribution functions of regression coefficients and scale parameters is presented. It is based on a general asymptotic formula of DiCiccio, Field & Fraser (1990) for approximating marginal tail probabilities. In developing the method, it is shown that the accuracy of the approximation of DiCiccio et al. can be substantially increased in certain cases by using a suitable change of variables, and a general approach to deriving such…

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位置参数 M 估计量的小样本渐近分布AI 解读

Asymptotic formulae for the distribution of M-estimators, i.e. maximum likelihood type estimators, of location, including the arithmetic mean, are derived which numerical studies show to give relative errors for densities and tail areas of the order of magnitude of 1% down to sample sizes 3 and 4 even in the extreme tails. The paper is the continuation of earlier work by the second author and is also closely related to Daniels's work on the saddlepoint approximation. The…

位置M估计量的小样本渐近分布AI 解读

Asymptotic formulae for the distribution of M-estimators, i.e. maximum likelihood type estimators, of location, including the arithmetic mean, are derived which numerical studies show to give relative errors for densities and tail areas of the order of magnitude of 1% down to sample sizes 3 and 4 even in the extreme tails. The paper is the continuation of earlier work by the second author and is also closely related to Daniels's work on the saddlepoint approximation. The…