Weighted Regression Curves for the Population with Diabetes Mellitus

Abstract

This study contributes to the fields of Statistics and Numerical Analysis by considering the Least Square method to develop new regression functions. Based on the chronological, discrete dataset for the national and international population with diabetes mellitus from 1980 through 2015, we examine the accuracy of various traditional regression functions. Moreover, in order to emphasize the importance of more recent, past data to the future outputs, the article suggests a weighted Least Square Method. The weight vector is developed by appropriately transforming the standard logistic function σ(t) on the given timeline while counting the number of node points, Then the root mean square error (RMSE) is re-defined along with these components and the derived weighted regression functions work more coincident than the non-weighted ones to predict the near future values. The obtained regression functions are employed again to predict the near (2016) and remote (2030) future populations of patients with diabetes and outputs are analyzed with their unique properties.