We proposed </span><span style="font-family:Verdana;">“</span><span style="font-family:Verdana;">a new extension of three</span><span style="font-family:Verda...We proposed </span><span style="font-family:Verdana;">“</span><span style="font-family:Verdana;">a new extension of three</span><span style="font-family:Verdana;">-</span><span style="font-family:Verdana;">parametric distribution” called the inverse power two-parameter weighted Lindley (IPWL) distribution capable of modeling a upside-down bathtub hazard rate function. This distribution is studied to get basic structural properties such as reliability measures, moments, inverse moments and its related measures. Simulation studies </span><span style="font-family:Verdana;">are </span><span style="font-family:Verdana;">done to present the performance and behavior of maximum likelihood estimates of the IPWL distribution parameters. Finally, we perform goodness of fit measures and test statistics using a real data set to show the performance of the new distribution.展开更多
文摘We proposed </span><span style="font-family:Verdana;">“</span><span style="font-family:Verdana;">a new extension of three</span><span style="font-family:Verdana;">-</span><span style="font-family:Verdana;">parametric distribution” called the inverse power two-parameter weighted Lindley (IPWL) distribution capable of modeling a upside-down bathtub hazard rate function. This distribution is studied to get basic structural properties such as reliability measures, moments, inverse moments and its related measures. Simulation studies </span><span style="font-family:Verdana;">are </span><span style="font-family:Verdana;">done to present the performance and behavior of maximum likelihood estimates of the IPWL distribution parameters. Finally, we perform goodness of fit measures and test statistics using a real data set to show the performance of the new distribution.
