Estimation of Parameters of Misclassified Size Biased Uniform Poisson Distribution and Its Application

Statistical data analysis is of great interest in every field ofmanagement, business, engineering, medicine, etc. At the time ofclassification and analysis, errors may arise, like aclassification of observation in the other class instead of theactual class. All fields of science and economics have substantialproblems due to misclassification errors in the observed data. Dueto a misclassification error in the data, the sampling process maynot suggest an appropriate probability distribution, and in thatcase, inference is impaired. When these types of errors areidentified in variables, it is expected to consider the problem’ssolution regarding classification errors. This paper presents thesituation where specific counts are reported erroneously asbelonging to other counts in the context of size biased UniformPoisson distribution, the so-called misclassified size biasedUniform Poisson distribution. Further, we have estimated theparameters of misclassified size biased Uniform Poissondistribution by applying the method of moments, maximum likelihoodmethod, and approximate Bayes estimation method. A simulationstudy is carried out to assess the performance of estimationmethods. A real dataset is discussed to demonstrate thesuitability and applicability of the proposed distribution in themodeling count dataset. A Monte Carlo simulation study ispresented to compare the estimators. The simulation results showthat the ML estimates perform better than their correspondingmoment estimates and approximate Bayes estimates.