## 6 Conclusions  
With the modern products becoming more and more durable, researchers have proposed different methods to cut the experimental time and cost, including the accelerated life tests and censoring. This study focuses on combining the CSPLAT and general progressive Type-II censoring to further reduce the time and money cost. Additionally, by adopting GIED (which has a versatile HRF) as the lifetime distribution and utilizing the general censoring methodology, we manage to provide extra flexibility in more complicated situations for the experimenters.  
We explore diverse approaches for estimating the parameters of GIED within the framework of a CSPALT based on general progressively censored samples. We delve into  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{\(\boldsymbol{\alpha}\)} & \(\boldsymbol{\lambda}\) & \(\boldsymbol{\beta}\) \\ \hline MLE & 0.4661745 & 1.304002 & 1.90858 \\ BE & 0.4856826 & 1.356861 & 2.099287 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Point estimation for \(\alpha,\lambda,\beta\) of the censored CSPALT samples.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{Normal condition (32 KV)} & & & & & & & \\
0.69 & 0.79 & 2.75 & 9.88 & 13.95 & 15.93 & 27.80 & 82.85 & 89.29 & 100.58 \\ Accelerated condition (36 KV) & & & & & & & & \\
0.96 & 0.99 & 1.97 & 2.07 & 2.58 & 2.71 & 3.67 & 3.99 & 13.77 & 25.50 \\ \hline \hline \end{tabular}
\end{table}
Table 5: General progressively Type-II censored samples with CS \(n=15,m=12,r=2\) and \(R_{1}=R_{2}=R_{3}=1,R_{4}=\cdots=R_{10}=0\).  
Figure 6: Empirical cumulative distribution (blue lines) and real CDF (red lines) plots for both conditions.  
both classical and Bayesian perspectives for estimating \(\lambda\) and \(\alpha\), representing the scale and shape parameter of GIED, as well as the acceleration factor \(\beta\). We devise maximum likelihood estimates and provide correlated ACIs by using the Fisher information matrix. Additionally, we derive point and interval estimates through Bayesian methods, leveraging importance sampling for point estimation and establishing Bayesian credible intervals. Monte-Carlo simulations are conducted under multiple censoring schemes to assess the validity and performance of the different methodologies. The results underscore the effectiveness of the mentioned approaches, with Bayesian point and interval estimates demonstrating better performance across most scenarios. The results also reveal the fact that both classical and Bayesian methods are not satisfying enough with small samples, but BEs and BCIs are slightly more effective since they have used the prior information. Finally, the proposed methods are applied on a real lifetime dataset, BCIs outperform the ACIs with shorter interval lengths, justifying that the Bayesian view is more suitable for this dataset, which is consistent with the previous results of the simulation and supports the suitability and applicability of our model.  
Moving forward, we aim to extend our investigation to encompass more intricate scenarios, such as step-stress partially accelerated life tests with general progressive Type-II censoring. We remain committed to exploring and developing more effective methods for estimating unknown parameters under diverse scenarios.  
Investigation, H.Z. and J.W.; Supervision, W.G. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Project S202410004203 which was supported by Beijing Training Program of Innovation and Entrepreneurship for Undergraduates. [PERSON]'s work was partially supported by the Science and Technology Research and Development Project of China State Railway Group Company, Ltd. (No. N2023Z020).  
Not applicable.  
The data presented in this study are openly available in [23].  
The authors declare no conflict of interest.