| Peer-Reviewed

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

Received: 16 November 2019     Accepted: 28 November 2019     Published: 10 December 2019
Views:       Downloads:
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.

Published in International Journal of Data Science and Analysis (Volume 5, Issue 6)
DOI 10.11648/j.ijdsa.20190506.15
Page(s) 136-142
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

Smoothing Technique, Cubic Spline, Kernel Smoothing

References
[1] C. and K., "Government printer," Kenya: Nairob, 2010.
[2] B. and H. J., "Kernel estimators of regression functions," Advances in econometrics: Fifth world congress, vol. 1, pp. 99--144, 1987.
[3] S. and. B. W., "Some aspects of the spline smoothing approach to non-parametric regression curve fitting," Journal of the Royal Statistical Society: Series B (Methodological, vol. 47, pp. 1--21, 1985.
[4] S. B. W. O. and O., "Spline smoothing: the equivalent variable kernel method," The Annals of Statistics, vol. 12, pp. 898--916, 1984.
[5] M. and C., "Choosing a smoothing parameter for a curve fitting by minimizing the expected prediction error," Acta Universitatis Apulensis, Mathematics-Informatics, vol. 5, pp. 91--96, 2003.
[6] Bowman, W. Adrian, H. Peter, T. and D., "Cross-validation in nonparametric estimation of probabilities and probability densitie," Biometrika, vol. 71, pp. 341--351, 1984.
[7] R. and M., "Empirical choice of histograms and kernel density estimators," Scandinavian Journal of Statistics}, pp. 65--78, 1982.
[8] H. P. a. S. S. J. a. J. M. a. M. and J.. S., "On optimal data-based bandwidth selection in kernel density estimation," Biometrika, vol. 78, pp. 263--269, 1991.
[9] Hardle, M. and J., "Bootstrap simultaneous error bars for nonparametric regression," The Annals of Statistics, pp. 778--796, 1991.
[10] G. J. a. L. and H., "Chiral perturbation theory: expansions in the mass of the strange quark," Nuclear Physics B, vol. 250, pp. 465--516, 1985.
[11] G. T. a. M. and H.-G., "Kernel estimation of regression functions," Smoothing techniques for curve estimation, pp. 23--68, 1979.
[12] N.. K. a. S. G. H. O. and O., "Serial and parallel processing of visual feature conjunctions," Nature, vol. 320, pp. 264--265, 1986.
[13] M. K. O. and O., "A comparison of a spline estimate to its equivalent kernel estimate," The Annals of Statistics, vol. 19, pp. 817--829, 1991.
[14] S. L. a. P. R. L. a. B. and G. E., "Harmonic splines for geomagnetic modelling," Physics of the Earth and Planetary Interiors, vol. 28, pp. 215--229, 1982.
[15] S.. R. a. M. W. P. a. V. D. B.. M. J. a. W. M. a. F. B. a. T. M. J. K. a. B.. A. A. a. M. C. a. B. and U. a. o., "Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma," New England Journal of Medicine, vol. 352, pp. 987--996, 2005.
Cite This Article
  • APA Style

    Lena Anyango Onyango, Thomas Mageto, Caroline Mugo. (2019). Determination of an Optimal Smoothing Technique for Maternal Health Care Statistics (A Case Study of Nakuru County 2012-2016). International Journal of Data Science and Analysis, 5(6), 136-142. https://doi.org/10.11648/j.ijdsa.20190506.15

    Copy | Download

    ACS Style

    Lena Anyango Onyango; Thomas Mageto; Caroline Mugo. Determination of an Optimal Smoothing Technique for Maternal Health Care Statistics (A Case Study of Nakuru County 2012-2016). Int. J. Data Sci. Anal. 2019, 5(6), 136-142. doi: 10.11648/j.ijdsa.20190506.15

    Copy | Download

    AMA Style

    Lena Anyango Onyango, Thomas Mageto, Caroline Mugo. Determination of an Optimal Smoothing Technique for Maternal Health Care Statistics (A Case Study of Nakuru County 2012-2016). Int J Data Sci Anal. 2019;5(6):136-142. doi: 10.11648/j.ijdsa.20190506.15

    Copy | Download

  • @article{10.11648/j.ijdsa.20190506.15,
      author = {Lena Anyango Onyango and Thomas Mageto and Caroline Mugo},
      title = {Determination of an Optimal Smoothing Technique for Maternal Health Care Statistics (A Case Study of Nakuru County 2012-2016)},
      journal = {International Journal of Data Science and Analysis},
      volume = {5},
      number = {6},
      pages = {136-142},
      doi = {10.11648/j.ijdsa.20190506.15},
      url = {https://doi.org/10.11648/j.ijdsa.20190506.15},
      eprint = {https://article.sciencepublishinggroup.com/pdf/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.},
     year = {2019}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Determination of an Optimal Smoothing Technique for Maternal Health Care Statistics (A Case Study of Nakuru County 2012-2016)
    AU  - Lena Anyango Onyango
    AU  - Thomas Mageto
    AU  - Caroline Mugo
    Y1  - 2019/12/10
    PY  - 2019
    N1  - https://doi.org/10.11648/j.ijdsa.20190506.15
    DO  - 10.11648/j.ijdsa.20190506.15
    T2  - International Journal of Data Science and Analysis
    JF  - International Journal of Data Science and Analysis
    JO  - International Journal of Data Science and Analysis
    SP  - 136
    EP  - 142
    PB  - Science Publishing Group
    SN  - 2575-1891
    UR  - https://doi.org/10.11648/j.ijdsa.20190506.15
    AB  - 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.
    VL  - 5
    IS  - 6
    ER  - 

    Copy | Download

Author Information
  • Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

  • Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

  • Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

  • Sections