### 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 |
---|---|

Pages (from-to) | 1715-1726 |

Number of pages | 12 |

Journal | Applied Mathematical Sciences |

Volume | 3 |

Issue number | 33-36 |

Publication status | Published - 2009 |

### Fingerprint

### Keywords

- Missing data
- Neyman allocation
- Winsorized mean

### ASJC Scopus subject areas

- Applied Mathematics

### Cite this

*Applied Mathematical Sciences*,

*3*(33-36), 1715-1726.

**New method to estimate missing data by using the asymmetrical winsorized mean in a time series.** / Razali, Ahmad Mahir; Al-Khazaleh, A. M H.

Research output: Contribution to journal › Article

*Applied Mathematical Sciences*, vol. 3, no. 33-36, pp. 1715-1726.

}

TY - JOUR

T1 - New method to estimate missing data by using the asymmetrical winsorized mean in a time series

AU - Razali, Ahmad Mahir

AU - Al-Khazaleh, A. M H

PY - 2009

Y1 - 2009

N2 - 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.

AB - 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.

KW - Missing data

KW - Neyman allocation

KW - Winsorized mean

UR - http://www.scopus.com/inward/record.url?scp=77950940507&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77950940507&partnerID=8YFLogxK

M3 - Article

VL - 3

SP - 1715

EP - 1726

JO - Applied Mathematical Sciences

JF - Applied Mathematical Sciences

SN - 1312-885X

IS - 33-36

ER -