Nonparametric Conditional Estimation
- Stanford Univ., Stanford, CA (United States)
Many nonparametric regression techniques (such as kernels, nearest neighbors, and smoothing splines) estimate the conditional mean of Y given X = z by a weighted sum of observed Y values, where observations with X values near z tend to have larger weights. In this report the weights are taken to represent a finite signed measure on the space of Y values. This measure is studied as an estimate of the conditional distribution of Y given X = z. Prom estimates of the conditional distribution, estimates of conditional means, standard deviations, quantiles and other statistical functionals may be computed.
- Research Organization:
- SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC)
- DOE Contract Number:
- AC02-76SF00515
- OSTI ID:
- 1454025
- Report Number(s):
- SLAC-R--309
- Country of Publication:
- United States
- Language:
- English
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