Determination of an Optimal Smoothing Technique for Maternal Health Care Statistics (A Case Study of Nakuru County 2012-2016)

Lena Anyango Onyango,Thomas Mageto,Caroline Mugo

doi:10.11648/j.ijdsa.20190506.15

Abstract

One of the Big four agenda is Universal health. This study focused on maternal health. The main aim of maternal health is usually to reduce maternal deaths. One way in aiding to reduce maternal deaths is to forecast maternal deaths using various statistical smoothing techniques. This would enable better future planning for example increase in health facilities. Shapiro-Wilk Normality Test confirmed that there was clear observable difference between the normal distribution and the data. The study hence focused on non-parametric regression methods which include Kernel and Cubic spline smoothing techniques which were applied on maternal health care data. The technique that best dealt with this type of data was identified and used to focus maternal deaths. Selecting an appropriate technique was important to achieve a good forecasting performance. The performance of the two smoothing technique was compared using MSE, MAE and RMSE and the best model identified. In both methods we have smoothing parameters. Selecting smoothing parameter goal is usually to base it on the data. According to the results obtained in the study, it is concluded that Cubic spline smoothing technique which has a lower MSE, MAE and RMSE is better than Kernel based smoothing technique. The statistical software that was used for the analysis was R. The study used maternal health care statistics data for Nakuru County.

Highlights

Smoothing is a type of data handling technique
At that time, smoothing techniques consisted of simple interpolation of the data and eventually evolved into more complex modern methods such as Cubic splines smoothing technique
The scatter normally compares the data to a standard normal distribution

Summary

Introduction

Smoothing is a type of data handling technique. In statistics, when data is smoothed, an approximation function is created that usually attempt to capture important patterns in the data. At that time, smoothing techniques consisted of simple interpolation of the data and eventually evolved into more complex modern methods such as Cubic splines smoothing technique. Spline smoothing in some sense corresponds approximately to bandwidth smoothing by a kernel method depending on the local design point density [4]. Consideration of kernel smoothing methods shows that there are desirable properties in how the effective local bandwidth acts in spline smoothing. To address the extreme handling of data, smoothing techniques should be used to achieve an accurate result in making predictions. There are many smoothing techniques available, and selecting the appropriate technique is an important issue to achieve a good forecasting

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Results

Conclusion