Abstract
It is possible to encounter missing value in a research. Missing value may be as a result of none response in primary data collection or unavailability of data in the case of secondary data. It may occur within a set of observations or at the tail end of the observations. The paper addresses missing value within a set of observations (interpolation). Existing methods considered are linear, log-linear, Catmull-Rom spline and cardinal spline and a method of estimating missing value is presented. For evaluation of the methods, random data are simulated using Monte Carlo simulation approach and analytical approach is used to determine the most effective method. Among the findings, linear, log-linear and the proposed method give high precision estimate compare to the CS and CSR methods. With the use of tension parameter, CS is better than CSR method.
Highlights
It is referred to as extrapolation if the observations are to be estimated beyond data collected but interpolation if the missing value is within a set of observations
In the estimation of interpolation, some of the commonly used methods are linear method, log-linear method, Catmull-Rom spline method, cubic spline method and cardinal spline method which were formulated by researchers in the past
This paper reviews the methods of estimating missing value within set of observations, compares for efficiency, that is, accuracy and proposes an alternative method of interpolation
Summary
It is referred to as extrapolation if the observations are to be estimated beyond data collected but interpolation if the missing value is within a set of observations. Non-response may be classified as missing value as the respondent has the sole right to supply information to be used for the research (Gelman and Hill [2]). The financial institutions in the country have records for financial activities for the period (see CBN Bulletin) Such information may not be accurate and difficult to verified. A number of researchers have worked to fill the gap in data collection which led to some existing methods of estimating extrapolation and interpolation, see Fung [5], Cheema [3], Iwueze et al [6]. This paper reviews the methods of estimating missing value within set of observations (interpolation), compares for efficiency, that is, accuracy and proposes an alternative method of interpolation
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