In this communication, we introduce a new statistical model and study its various mathematical properties. The expressions for hazard rate, reversed hazard rate, and odd functions are provided. We explore the asymptotic behaviors of the density and hazard functions of the newly proposed model. Further, moments, median, quantile, and mode are obtained. The cumulative distribution and density functions of the general $k$th order statistic are provided. Sufficient conditions, under which the likelihood ratio order between two inverse generalized linear failure rate (IGLFR) distributed random variables holds, are derived. In addition to these results, we introduce several estimates for the parameters of IGLFR distribution. The maximum likelihood and maximum product spacings estimates are proposed. Bayes estimates are calculated with respect to the squared error loss function. Further, asymptotic confidence and Bayesian credible intervals are obtained. To observe the performance of the proposed estimates, we carry out a Monte Carlo simulation using $R$ software. Finally, two real-life data sets are considered for the purpose of illustration.

翻译：在这篇论文中，我们引入了一种新的统计模型，并研究了它的各种数学特性。我们提供了灾难率、反向灾难率和奇函数的表达式。我们探索了新提出的模型的密度和危险函数的渐近行为。进一步获得了矩、中位数、分位数和模数。为IGLFR分布的一般第k个顺序统计量提供了累积分布和密度函数。导出了两个逆广义线性故障率（IGLFR）分布随机变量之间似然比次序成立的充分条件。除了这些结果外，我们还提出了几种估计IGLFR分布参数的方法。我们提出了最大似然估计、最大产品间距估计。Bayes估计是针对平方误差损失函数计算的。进一步获得了渐近置信和Bayesian可信区间。为了观察所提估计的性能，我们使用R软件进行了Monte Carlo模拟。最后，考虑了两个真实数据集作为说明目的。