Alex mean (AlM) location estimator for measure of Center of data

Abstract

Computational data analysis is an essential component of modern statistics. The most important challenge in classical statistics was an estimation of the location parameter. In addition, the selection of estimators among potential estimators is another problem in the computational data analysis. Therefore, the main objective of this study is to develop a location estimator that has been modified compared to all common location estimators. In order to ensure the objective of the new estimator, combinations weighted mean (Alex-mean) was developed and computed with R version 3.6.3. Further, the properties of a good location estimator diagnosed with all location estimators compared to Alex-mean with a COVID-19 new case data set. In addition, the new estimator was compared with mean, median and trimmed-mean based on relative efficiency and bootstrap simulation techniques. The new estimator is better than the mean, median and trimmed-mean of relative efficiency estimated using bootstrap and simulation techniques. The sensitivity graph shows that Alex-mean is insensitive to the outlier and is computed on the basis of all observations. Thus, the researcher suggested that Alex-mean is the good location estimator than all common measures of central tendency since it is undisturbed by extreme values.

Keywords:

• Alex-mean
• Computational statistics
• Efficient estimator
• Location estimator
• Summarizing Covid-19
• Statistics