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递归蒙特卡洛滤波：算法与理论分析AI 解读

Recursive Monte Carlo filters, also called particle filters, are a powerful tool to perform computations in general state space models. We discuss and compare the accept–reject version with the more common sampling importance resampling version of the algorithm. In particular, we show how auxiliary variable methods and stratification can be used in the accept–reject version, and we compare different resampling techniques. In a second part, we show laws of large numbers and a…

一般回归模型中条件无偏的有界影响估计及其广义线性模型应用AI 解读

In this article robust estimation in generalized linear models for the dependence of a response y on an explanatory variable x is studied. A subclass of the class of M estimators is defined by imposing the restriction that the score function must be conditionally unbiased, given x. Within this class of conditionally Fisher-consistent estimators, optimal bounded-influence estimators of regression parameters are identified, and their asymptotic properties are studied. The…

二维格点上的内蕴自回归及相关模型AI 解读

Stationary autoregressions on a two-dimensional lattice are generalized to intrinsic models where only increments are assumed to be stationary. Prediction formulae and the asymptotic behaviour of the semivariogram are derived. For parameter estimation we propose an approximate maximum likelihood estimator, a generalization of Whittle's estimator; it is derived also for general intrinsic models.