Robust Location Retrieval Strategy Under Systematic Sampling

Systematic sampling design is an ordered observation scheme that is popular in data collection because it has the property of uniform coverage and is operationally simple. This scheme is, however, susceptible to extreme observations, which may severely compromise the accuracy of traditional location estimators. To overcome this weakness, this research proposes a robust location retrieval or estimation method that regulates the impact of unusual observations in the ordered selection framework. In the suggested strategy, a set of twenty influence-adjusted estimators is built with a variety of re-descending weighting functions, which is then extended with another family of five generalized ones. Large-scale derivations of mean squared error (MSE) and percentage relative efficiency (PRE) are used to explore the theoretical properties of the proposed estimators. Significant improvements in stability and efficiency over existing methods are demonstrated by extensive empirical analyses, both with real data (e.g., mtcars and Trees) and on a wide variety of synthetic problems containing embedded outliers. The findings suggest that location retrieval based on influence-controlled processes is much more robust when using an ordered observation scheme and can be an efficient and scalable tool implemented in the modern data-intensive and computationally demanding environment.