Statistical Inference for Conditional Curves: Poisson Process Approach
The Annals of Statistics · 1992 · Volume 20 · Issue 2
Abstract
A Poisson approximation of a truncated, empirical point process enables us to reduce conditional statistical problems to unconditional ones. Let $(\mathbf{X,Y})$ be a $(d + m)$-dimensional random vector and denote by $F(\cdot\mid\mathbf{x})$ the conditional d.f. of $\mathbf{Y}$ given $\mathbf{X} = \mathbf{x}$. Applying our approach, one may study the fairly general problem of evaluating a functional parameter $T(F(\cdot\mid\mathbf{x}_1),\ldots,F(\cdot\mid\mathbf{x}_p))$ based on independent replicas $(\mathbf{X}_1,\mathbf{Y}_1),\ldots,(\mathbf{X}_n,\mathbf{Y}_n)$ of $(\mathbf{X,Y})$. This will be exemplified in the particular cases of nonparametric estimation of regression means and regression quantiles besides other functionals.
Keywords
60G55; 62J99; empirical process; Hellinger distance; Poisson process; Regression functionals;
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