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用于小区域估计的 M 分位数模型AI 解读

Small area estimation techniques typically rely on regression models that use both covariates and random effects to explain variation between the areas. However, such models also depend on strong distributional assumptions, require a formal specification of the random part of the model and do not easily allow for outlier-robust inference. We describe a new approach to small area estimation that is based on modelling quantilelike parameters of the conditional distribution of…

M 分位数AI 解读

It is well known that a M-estimator of the centre of symmetry Θ of a symmetric distribution can be defined in terms of either a continuous symmetric loss function ρ or the associated influence functionψ. This estimator is robust if ψ is bounded. In this paper, we develop a generalization of the M-concept to estimation of a quantile analogue of Θ, called a M-quantile, by introducing a particular kind of asymmetry into ψ. A natural consequence is that the M- quantile parameter…