A new single-class demand unconstraining method
The authors propose a new statistical unconstraining method which is based on the construction of the distribution function for the censored demand and application of the maximum likelihood approach to estimate distribution parameters. Numerical results are presented of comparative analysis of existing unconstraining methods and the method advocated in the paper. It is demonstrated that the new method has proven to be more efficient in the case of a high percentage of observed censored elements of sample data. Yet another important advantage of the method connected to the fact that it enables one to process the situation of censoring information incompleteness when some elements of the observed sample data are known to be censored or not and for the others this information is not available. Mathematical computer environment Wolfram Mathematica has been used for obtaining all the results presented in the paper.