New method to estimate missing data by using the asymmetrical winsorized mean in a time series

Ahmad Mahir Razali, A. M H Al-Khazaleh

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

In this paper we consider the problem of missing data in a time series analysis. We propose asymmetrical r ≠ s winsorized mean to handle the problem of missing data. Beside that we suggested the Neyman allocation method to choose the values of r and s in asymmetric winsorized mean. We used the a absolute mean error and mean square error to compare the result of estimation missing data with other methods, such as trends, average of the whole data, naive forecast and average bound of the holes and simultaneous filling in the missing data. An example had been presented.

Original language English 1715-1726 12 Applied Mathematical Sciences 3 33-36 Published - 2009

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Time series analysis
Missing Data
Mean square error
Time series
Estimate
Neyman Allocation
Time Series Analysis
Forecast
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Keywords

• Missing data
• Neyman allocation
• Winsorized mean

ASJC Scopus subject areas

• Applied Mathematics

Cite this

In: Applied Mathematical Sciences, Vol. 3, No. 33-36, 2009, p. 1715-1726.

Research output: Contribution to journalArticle

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