![]() ![]() Open Access This is an open access article under the CC BY-NC license ( ). The three types of estimators are then compared using simulation studies and finally a Swedish fertility dataset was modeled using a GIG distribution. Parameter estimation is done using method of moments, empirical probability generating function based method and maximum likelihood estimation approach. ![]() This work deals with parameter estimation of a Geometric distribution inflated at certain counts, which we called Generalized Inflated Geometric (GIG) distribution. Geometric distribution is a special case of Negative Binomial distribution. Generally, Inflated Poisson or Inflated Negative Binomial distribution are the most commonly used for modeling and analyzing such data. The geometric distribution is the probability distribution of the number of failures we get by repeating a Bernoulli experiment until we obtain the first. So to get appropriate results from them and to overcome the possible anomalies in parameter estimation, we may need to consider suitable inflated distribution. If a random sample of 3,308 from this population yields a sample total ( y1+y2+.+yn) of 5801, then estimate p. Determine the maximum likelihood estimator of p. ![]() But these inflated frequencies at particular counts may lead to overdispersion and thus may cause difficulty in data analysis. Determine the method of moment estimator of p. A count data that have excess number of zeros, ones, twos or threes are commonplace in experimental studies. In probability theory and statistics, a geometric distribution is one of two discrete probability distributions: Probability distribution of the number of. ![]()
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