Methods for Estimating Missing Values in Descriptive Time Series Statistics: Novelty and Efficiency under Buys-Ballot

Abstract

There is dearth of information in the field of statistics on the innovative estimation methods that can replace missing values in descriptive time series data. Therefore, this review work provides information on the existing and new methods of estimating missing values in descriptive time series data. The work provides new insight on the comparative performance of the recently-developed methods and the existing ones and discussed model structure and trending curves as important parameters in estimation of missing values. It is expected that the present contribution will assist statisticians seeking to solve the problem of missing values in descriptive time series data. The application of this work should be restricted to time series data with trend (linear, quadratic and exponential) and seasonal components combined in the additive and multiplicative forms. The contribution covers data missing at one point at a time in a row or column when data are arranged in a Buys-Ballot table. Use of the Buys-Ballot table arrangement in the estimation of missing values is new, convenient and merits scientific analysis.