Approximation of Finite Population Totals Using Lagrange Polynomial

Kabareh, Lamin; Mageto, Thomas; Muema, Benjamin Kyalo

doi:10.4236/ojs.2017.74048

Cited by 2 publications

(4 citation statements)

References 18 publications

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“…A study conducted by Gotelli has considered the geometric and exponential models and further considered incorporating the deaths and births [5]. Another study conducted by Kabareh et al considered the estimation of the population total using the birth and death process [6]. However, due to the unavailability of births and deaths data in most of the administrative units due to the beliefs, religion and nonresponse on the vital statistics, the estimation using such information is possible in developed countries where the records are available and complete.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In estimating the Kenya population total for the year 2019, the study by Kabareh et al estimated the population total using the Lagrange polynomial and estimated the value of forty eight thousand five hundred thirty three thousand five hundred eighty seven that is close but not good enough to the actual census survey value of forty seven thousand five hundred sixty for thousand two hundred ninety six [7]. The study on population total has also been investigated by Kabareh et al in which the piecewise polynomial approximation to the Newton backward difference polynomial approximation of the finite population total considered and results compared [8].…”

Section: Literature Reviewmentioning

confidence: 99%

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Estimating Population Total Using Machine Learning Logistic Regression: COVID-19 Pandemic Challenges Perspective

Mageto¹

2021

AJTAS

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The estimation of the population total in undeveloped and developing countries in the recent past has attracted a lot of interest to many researchers due to the sole purpose of planning resource allocation, personnel training and infrastructure in social, health, transport, communication and education. The comprehensive census survey in many countries are conducted every ten years but the government administration changes in many counties every four to five years due to the limit of government terms as per the constitution and therefore does not coincide with the time of census survey. Further, due to the emerging COVID-19 pandemic challenges that requires ministry of health protocols of social distance, the census survey in which the methods of questionnaire and personal interview are commonly used need to be avoided and therefore there is need to search for a better and reliable estimating models for estimating the population total which is the main focus of the study. The existing and developed methods of exponential and logistic class of population total estimating modes have been considered and compared. The main problem in the logistic models in estimating the population total is the estimation of the highest possible population that can be attained for each of the administrative units. In this study a machine learning logistic regression has been proposed and incorporated to search and estimate the constant using the supervised learning process. The performance of the methods have been compared using the Root Mean Square Error (RMSE) whose values were recorded as 1.062, 1.524, 0.477, 0.819 and 0.286 for the exponential, logistic I, Logistic II, logistic III and machine learning logistic (logistic IV) in which the proposed model performed better with the least square error value of 0.286. The proposed model was then used to project the population total and projected the population total for all regions as 51.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Estimating Population Total Using Machine Learning Logistic Regression: COVID-19 Pandemic Challenges Perspective

Mageto¹

2021

AJTAS

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“…if function   f T is continuous on a closed and bounded interval   , a b (Kabareh, Mageto & Muema, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…The study approximated the population of 2009 accurately as it was in the national census results. Again, it makes a projection that Kenyan population for the year 2019 census will be forty-eight million five hundred and thirtythree thousand five hundred and eighty-seven (Kabareh, Mageto & Muema, 2017).…”

Section: Introductionmentioning

confidence: 99%

Application of Lagrange Polynomial for Interpolating Income Generation from Certain Students’ Fee Structure

Kira¹

2019

MTM

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This paper presents the findings of the study that aimed to examine the changing aspects of income generation from certain students' fee structure. It used an empirical example of Higher learning institution from Tanzania to determine whether Lagrange interpolation method would emerge a useful tool for interpolating and extrapolating enrolments of undergraduate students. The functional values evaluated at all nodes on the given data set accurately match the enrolments records of each respective academic year, though with nonconformities at the additional

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Approximation of Finite Population Totals Using Lagrange Polynomial

Cited by 2 publications

References 18 publications

Estimating Population Total Using Machine Learning Logistic Regression: COVID-19 Pandemic Challenges Perspective

Estimating Population Total Using Machine Learning Logistic Regression: COVID-19 Pandemic Challenges Perspective

Application of Lagrange Polynomial for Interpolating Income Generation from Certain Students’ Fee Structure

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