Inverse Lindley power series distributions: a new compounding family and regression model with censored data

Mohammed K Shakhatreh; Sanku Dey; Devendra Kumar

doi:10.1080/02664763.2021.1951683

Inverse Lindley power series distributions: a new compounding family and regression model with censored data

J Appl Stat. 2021 Aug 31;49(13):3451-3476. doi: 10.1080/02664763.2021.1951683. eCollection 2022.

Authors

Mohammed K Shakhatreh¹, Sanku Dey², Devendra Kumar³

Affiliations

¹ Department of Mathematics and Statistics, Jordan University of Science and Technology, Irbid, Jordan.
² Department of Statistics, St. Anthony's College, Shillong, India.
³ Department of Statistics, Central University of Haryana, Mahendergarh, India.

Abstract

This paper introduces a new class of distributions by compounding the inverse Lindley distribution and power series distributions which is called compound inverse Lindley power series (CILPS) distributions. An important feature of this distribution is that the lifetime of the component associated with a particular risk is not observable, rather only the minimum lifetime value among all risks is observable. Further, these distributions exhibit an unimodal failure rate. Various properties of the distribution are derived. Besides, two special models of the new family are investigated. The model parameters of the two sub-models of the new family are obtained by the methods of maximum likelihood, least square, weighted least square and maximum product of spacing and compared them using the Monte Carlo simulation study. Besides, the log compound inverse Lindley regression model for censored data is proposed. Three real data sets are analyzed to illustrate the flexibility and importance of the proposed models.

Keywords: Lindley distribution; Monte Carlo simulation; inverse Lindley power series distributions; maximum-likelihood estimators; regression model.