Steady state adjusting trends using a data-driven local polynomial regression

Fritz, Marlon

doi:10.1016/j.econmod.2019.08.018

Cited by 2 publications

(1 citation statement)

References 42 publications

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“…A total of 963 households comprising of 63.6 per cent had complete interviews, 43 households comprising of 3.3 percent gave partial answers while 457 households (30.2 per cent) of the households could not be accessed. The other data source of auxiliary population characteristics from the clusters in question were obtained from the records of previous survey [18][19][20].…”

Section: Resultsmentioning

confidence: 99%

Estimating Total Energy Demand from Incomplete Data Using Non-parametric Analysis

Warutumo¹,

Kihara²,

Mbugua³

2020

IJDSA

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The validity and usefulness of empirical data requires that the data analyst ascertains the cleanliness of the collected data before any statistical analysis commence. In this study, petroleum demand data for a period of 24 hours was collected from 1515 households in 10 clusters. The primary sampling units were stratified into three economic classes of which 50% were drawn from low class, 28% from medium class and 22% from high class. 63.6% of the questionnaires were completed whereas incomplete data was computed using multivariate imputation by chained equation with the aid of auxiliary information from past survey. The proportion of missing data and its pattern was ascertained. The study assumed that missing data was at random. Nonparametric methods namely Nadaraya Watson, Local Polynomial and a design estimator Horvitz Thompson were fitted to aid in the estimation of the total demand for petroleum which has no close substitute. The performance of the three estimators were compared and the study found that the Local Polynomial approach appeared to be more efficient and competitive with low bias. Local polynomial estimator took care of the boundary bias better as compared to Nadaraya Watson and Horvitz Thompson estimators. The results were used to estimate the long time gaps in petroleum demand in Nairobi county, Kenya.

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Section: Resultsmentioning

confidence: 99%