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On Bootstrap Confidence Intervals Associated with Nonparametric Regression Estimators for A Finite Population Total
| Content Provider | Semantic Scholar |
|---|---|
| Author | Makumi, Nicholas Odhiambo, Romanus Orwa, G. O. Adhiambo, Stellamaris |
| Copyright Year | 2017 |
| Abstract | The precision of an estimator is at times discussed regarding the variance. Usually, the exact value of the variance is unknown. The discussion relies on unknown populace quantities. When a researcher obtains the survey data, an estimate of the variance can, therefore, be calculated. When survey results are presented, it is good practice to provide variance estimates for the estimator used in the study. The estimator of the variance can further be used to construct confidence interval, assuming that the sampling distribution of estimator is approximately normal. This study proposes estimation of standard error and confidence interval for a nonparametric regression estimator for a finite population using bootstrapping method. The idea behind bootstrapping is to carry out computations on the collected data. Computation activity assists in estimating the disparity of statistics that are themselves computed from the same data. The variance of the Nadaraya-Watson estimator is derived, based on bootstrap procedure. This operation has led to the derivation of confidence interval associated with Nadaraya-Watson estimator of the population total. A simulation study has been carried out. The overall conclusion is that the confidence interval associated with Nadaraya-Watson estimator is tighter than all the other estimators (Horvitz-Thompson estimator, Local linear estimator, and Ratio estimator). |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20170602.17.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
