Statistical inference for the geometric distribution based on δδ-records

New inferential procedures for the geometric distribution, based on δδ-records, are developed. Maximum likelihood and Bayesian approaches for parameter estimation and prediction of future records are considered. The performance of the estimators is compared with those based solely on record-breaking data by means of Monte Carlo simulations, concluding that the use of δδ-records is clearly advantageous. An example using real data is also discussed.