Lifetime distributions are an important statistical tools to model the different characteristics
of lifetime data sets. The statistical literature contains very sophisticated distributions to analyze
these kind of data sets. However, these distributions have many parameters which
cause a problem in estimation step. To open a new opportunity in modeling these kind of
data sets, we propose a new extension of half-logistic distribution by using the odd Lindley-
G family of distributions. The proposed distribution has only one parameter and simple
mathematical forms. The statistical properties of the proposed distributions, including complete
and incomplete moments, quantile function and Re´ nyi entropy, are studied in detail.
The unknown model parameter is estimated by using the different estimation methods,
namely, maximum likelihood, least square, weighted least square and Cramer-von Mises.
The extensive simulation study is given to compare the finite sample performance of parameter
estimation methods based on the complete and progressive Type-II censored samples.
Additionally, a new log-location-scale regression model is introduced based on a new distribution.
The residual analysis of a new regression model is given comprehensively. To convince
the readers in favour of the proposed distribution, three real data sets are analyzed
and compared with competitive models. Empirical findings show that the proposed oneparameter
lifetime distribution produces better results than the other extensions of halflogistic
distribution.