American Journal of Mathematical and Computer Modelling
Volume 5, Issue 1, March 2020, Pages: 12-17
Received: Jan. 7, 2020;
Accepted: Jan. 27, 2020;
Published: Feb. 11, 2020
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Kelechukwu Celestine Nosike Dozie, Department of Statistics Imo State University, Owerri, Imo State, Nigeria
Maxwell Azubuike Ijomah, Department of Mathematics/Statistics, University of Port Harcourt, Port Harcourt, Nigeria
Time series analyses are statistical methods used to assess trends in repeated measurements taken at equally spaced time intervals and their relationships with other trends or events, taking account of the temporal structure of such data. An important aspect of descriptive time series analysis is the choice of model for time series decomposition. This paper examined the challenges in choosing between additive and mixed models in time series decomposition. Most of the existing studies have focused on how to choose between additive and multiplicative models with little or no regards on mixed model. The ultimate objective of this study is therefore, to compare the row, column and overall means and variances of the Buys-Ballot table for additive and mixed models. Table 1 shows that the column variances of Buys-Ballot table is constant for additive model but depends on slope and seasonal effects for mixed model. Results show that seasonal variances of the Buys-Ballot table is constant for additive model and a function of slope and seasonal effects for mixed model. Also, when there is no trend (b=0), the estimates of row, column and overall means are the same for the two models while the estimates of seasonal indices are not the same for both additive and mixed models.
Kelechukwu Celestine Nosike Dozie,
Maxwell Azubuike Ijomah,
A Comparative Study on Additive and Mixed Models in Descriptive Time Series, American Journal of Mathematical and Computer Modelling.
Vol. 5, No. 1,
2020, pp. 12-17.
Copyright © 2020 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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