In Kenya life insurance has contributed widely and still remains a vital aspect of the social-economic development of the society. It focuses on safe- guarding the future as well as ensuring that there is some savings that can be used later in life. Despite its importance, the penetration of life insurance is currently only at one point three percent in Kenya. This is a small percentage in comparison to the developed countries where life insurance penetration is quite high. In this research, local regression (LOESS) method was used. LOESS specifically denotes a method that is also known as locally weighted polynomial regression. At each point in the data set a low-degree polynomial is fitted to a subset of the data, with explanatory variable values near the point whose response is being estimated. In local polynomial regression, a low-order weighted least squares(WLS) regression is fit at each point of interest using data from some neighbourhood around x. The value of the regression function for the point is then obtained by evaluating the local polynomial using the explanatory variable values for that data point. In this research income, education, age and marital status were found as the major factors associated with low insurance intake in Uasin Gishu County. The research highly recommends the insurance companies to apply the knowledge of LOESS to determine the major factors associated with low life insurance uptake in the country. Insurance companies should strive to provide educative seminars to the public to increase life insurance uptake. In this research we had uptake of life insurance as dependent variable and level of income, education level, age and marital status being independent variables.
| Published in | American Journal of Theoretical and Applied Statistics (Volume 5, Issue 6) |
| DOI | 10.11648/j.ajtas.20160506.14 |
| Page(s) | 354-358 |
| 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), 2016. Published by Science Publishing Group |
Locally Weighted Regression, LOESS, Multivariate Loess Surface, Multiple Local Polynomial Regression
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APA Style
Willy Thuitai, Anthony Gichuhi Waititu, Anthony Wanjoya. (2016). Multiple Local Polynomial Regression Modelling: Case of Life Insurance Uptake in Kenya, Uasin Gishu County. American Journal of Theoretical and Applied Statistics, 5(6), 354-358. https://doi.org/10.11648/j.ajtas.20160506.14
ACS Style
Willy Thuitai; Anthony Gichuhi Waititu; Anthony Wanjoya. Multiple Local Polynomial Regression Modelling: Case of Life Insurance Uptake in Kenya, Uasin Gishu County. Am. J. Theor. Appl. Stat. 2016, 5(6), 354-358. doi: 10.11648/j.ajtas.20160506.14
AMA Style
Willy Thuitai, Anthony Gichuhi Waititu, Anthony Wanjoya. Multiple Local Polynomial Regression Modelling: Case of Life Insurance Uptake in Kenya, Uasin Gishu County. Am J Theor Appl Stat. 2016;5(6):354-358. doi: 10.11648/j.ajtas.20160506.14
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Download@article{10.11648/j.ajtas.20160506.14,
author = {Willy Thuitai and Anthony Gichuhi Waititu and Anthony Wanjoya},
title = {Multiple Local Polynomial Regression Modelling: Case of Life Insurance Uptake in Kenya, Uasin Gishu County},
journal = {American Journal of Theoretical and Applied Statistics},
volume = {5},
number = {6},
pages = {354-358},
doi = {10.11648/j.ajtas.20160506.14},
url = {https://doi.org/10.11648/j.ajtas.20160506.14},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20160506.14},
abstract = {In Kenya life insurance has contributed widely and still remains a vital aspect of the social-economic development of the society. It focuses on safe- guarding the future as well as ensuring that there is some savings that can be used later in life. Despite its importance, the penetration of life insurance is currently only at one point three percent in Kenya. This is a small percentage in comparison to the developed countries where life insurance penetration is quite high. In this research, local regression (LOESS) method was used. LOESS specifically denotes a method that is also known as locally weighted polynomial regression. At each point in the data set a low-degree polynomial is fitted to a subset of the data, with explanatory variable values near the point whose response is being estimated. In local polynomial regression, a low-order weighted least squares(WLS) regression is fit at each point of interest using data from some neighbourhood around x. The value of the regression function for the point is then obtained by evaluating the local polynomial using the explanatory variable values for that data point. In this research income, education, age and marital status were found as the major factors associated with low insurance intake in Uasin Gishu County. The research highly recommends the insurance companies to apply the knowledge of LOESS to determine the major factors associated with low life insurance uptake in the country. Insurance companies should strive to provide educative seminars to the public to increase life insurance uptake. In this research we had uptake of life insurance as dependent variable and level of income, education level, age and marital status being independent variables.},
year = {2016}
}
TY - JOUR T1 - Multiple Local Polynomial Regression Modelling: Case of Life Insurance Uptake in Kenya, Uasin Gishu County AU - Willy Thuitai AU - Anthony Gichuhi Waititu AU - Anthony Wanjoya Y1 - 2016/10/27 PY - 2016 N1 - https://doi.org/10.11648/j.ajtas.20160506.14 DO - 10.11648/j.ajtas.20160506.14 T2 - American Journal of Theoretical and Applied Statistics JF - American Journal of Theoretical and Applied Statistics JO - American Journal of Theoretical and Applied Statistics SP - 354 EP - 358 PB - Science Publishing Group SN - 2326-9006 UR - https://doi.org/10.11648/j.ajtas.20160506.14 AB - In Kenya life insurance has contributed widely and still remains a vital aspect of the social-economic development of the society. It focuses on safe- guarding the future as well as ensuring that there is some savings that can be used later in life. Despite its importance, the penetration of life insurance is currently only at one point three percent in Kenya. This is a small percentage in comparison to the developed countries where life insurance penetration is quite high. In this research, local regression (LOESS) method was used. LOESS specifically denotes a method that is also known as locally weighted polynomial regression. At each point in the data set a low-degree polynomial is fitted to a subset of the data, with explanatory variable values near the point whose response is being estimated. In local polynomial regression, a low-order weighted least squares(WLS) regression is fit at each point of interest using data from some neighbourhood around x. The value of the regression function for the point is then obtained by evaluating the local polynomial using the explanatory variable values for that data point. In this research income, education, age and marital status were found as the major factors associated with low insurance intake in Uasin Gishu County. The research highly recommends the insurance companies to apply the knowledge of LOESS to determine the major factors associated with low life insurance uptake in the country. Insurance companies should strive to provide educative seminars to the public to increase life insurance uptake. In this research we had uptake of life insurance as dependent variable and level of income, education level, age and marital status being independent variables. VL - 5 IS - 6 ER -
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