Summary
For fixed design regression data kernel estimation is widely used to estimate μ(v)(t), thev-th derivative of μ(t). Denoting such an estimator by\(\hat \mu _{nv} (t),\) this paper is concerned with the almost sure convergence of\(\hat \mu _{nv} (t) - E\hat \mu _{nv} (t)\). It is shown that under several inserting procedures a law of iterated logarithm holds.
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Chen, ZG., Gasser, T. A law of the iterated logarithm for kernel estimators of regression functions. Probab. Th. Rel. Fields 93, 285–296 (1992). https://doi.org/10.1007/BF01193053
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DOI: https://doi.org/10.1007/BF01193053
Keywords
- Stochastic Process
- Probability Theory
- Mathematical Biology
- Regression Function
- Kernel Estimation