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In this paper, we investigate kernel regression estimation when the data are contaminated by measurement errors in the context of random fields. We establish sharp rate of weak and strong… Click to show full abstract
In this paper, we investigate kernel regression estimation when the data are contaminated by measurement errors in the context of random fields. We establish sharp rate of weak and strong convergence of the kernel regression estimator under both the ordinary smooth and super-smooth assumptions. Numerical studies were carried out in order to illustrate the performance of the estimator with simulated data.
Keywords:
regression;
random fields;
kernel regression;
regression estimation;
estimation errors
Journal Title: Afrika Matematika
Year Published: 2019
Link to full text (if available)
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Journal Title: Afrika Matematika
Year Published: 2019
Link to full text (if available)
Share on Social Media: Sign Up to like & get
recommendations!