ESTIMATE CONFIDENE INTERAVALS FOR MONOTONE NONPARAMETRIC REGRESSION | ||
| Diyala Journal For Pure Science | ||
| Article 1, Volume 10, Issue 1, February 2014, Pages 13-28 | ||
| Authors | ||
| ; Asmaa. N.Abdulla | ||
| Abstract | ||
| Monotonic regression is a nonparametric method designed for application in which the expected value of a response variable increases or decreases in relation to one or more explanatory. One approach is to first apply nonparametric regression to data Estimation of a response variable as a function of two continuous predictor variables is considered and then monotone smooth the initial estimates to ًiron outً violations to the assumed order. Here, such estimators are considered, where local polynomial regression is first used, followed by monotone method using simple averages( SAT) and we use bootstrap procedure and compare by this methods through Monte Carlo simulation using mean – squared error(MSE) . The primary focus of this work is to estimate different types of confidence intervals for these monotone nonparametric regression estimators Most of the confidence intervals use local polynomial regression and resampling methods bootstrap procedure and suggested method and combine between this methods, the methods are then applied for a real data in air pollution field. | ||
| Keywords | ||
| local polynomial regression; monotone regression; simple averages methods; least squares isotonic regression; pool; bootstrap; PAV | ||
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