Evaluation of Methods of Interpolation (Existing and Proposed) using Monte Carlo Simulation Technique and Delete-D Jackknife Method
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.
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