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Let $$\left\{ (X_{i},Y_{i}), i \ge 1 \right\} $$(Xi,Yi),i≥1 be a strictly stationary sequence of associated random vectors distributed as (X, Y). This note deals with kernel estimation of the regression function… Click to show full abstract
Let $$\left\{ (X_{i},Y_{i}), i \ge 1 \right\} $$(Xi,Yi),i≥1 be a strictly stationary sequence of associated random vectors distributed as (X, Y). This note deals with kernel estimation of the regression function $$r(x)=\mathbb {E}[Y|X=x]$$r(x)=E[Y|X=x] in the presence of randomly right censored data caused by another variable C. For this model we establish a strong uniform consistency rate of the proposed estimator, say $$r_{n}(x)$$rn(x). Simulations are drawn to illustrate the results and to show how the estimator behaves for moderate sample sizes.
Journal Title: Statistical Papers
Year Published: 2018
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