本文探討半導體與電子產品中，兼具早衰（infant mortality）與長期磨耗 （wear-out）雙重失效模式之多子群體（sub-populations）產品的壽命分析。早衰失效多源於製程疏失或原料缺陷所產生的瑕疵品（defective units），因其在檢驗階段仍能正常運作而難以偵測，嚴重影響企業售後維修成本。為有效分析此類資料，本文基於廣義有限失效母體（generalized limited failure population， GLFP）模型，針對實務上多家不同廠牌製造所形成的多子群體資料進行深入探討。在兩失效模式皆服從獨立對數常態壽命分布之假設下，利用資料增補 (data augmentation) 的方法建構完整概似函數(complete likelihood function)，並結合共軛結構優勢，於階層貝氏架構（hierarchical Bayesian framework）下引入三種不同的隨機效應模型（random-effects models），以精準捕捉並描述廠牌間的產品異質性。此方法不僅能在大量設限（censoring）資料下發揮資訊借用（borrowing the strength）的功效，且可同時提供各廠牌產品完整的可靠度推論，並大幅提升馬可夫鏈蒙地卡羅（Markov chain Monte Carlo， MCMC）演算法之計算效率。經由模擬結果驗證，本方法在模型選擇上具有高度穩健性。最後，本文應用 Backblaze 公司之硬碟資料進行分析，藉由四種壽命指標評比各廠牌產品之優劣，作為企業優化線上品管與進貨決策的參考依據。;This thesis considers product lifetime analysis for multiple sub-populations in semiconductor and electronic products. These products usually exhibit infant mortality and long-term wear-out failure modes. Infant mortality failures typically originate from defective units caused by manufacturing or raw material flaws. Because these units function normally during the initial screening stage， they are difficult to detect， which severely impacts post-sale warranty and maintenance costs. To effectively analyze such lifetime data， this study builds upon the generalized limited failure population (GLFP) model to conduct an in-depth investigation into multi-subpopulation data generated by various brands in practice. Under the assumption that both failure modes follow independent lognormal lifetime distributions， a complete likelihood function is constructed using data augmentation. Taking advantages of conjugate structures， three random-effects models are introduced within a hierarchical Bayesian framework to precisely capture and characterize the product heterogeneity among different brands. The proposed approach not only borrows strength under heavy censoring to provide comprehensive reliability inferences for each brand， but also substantially enhances the computational efficiency of the Markov chain Monte Carlo (MCMC) algorithm. Simulation results demonstrate that the proposed method possesses high robustness in model selection. Finally， the methodology is applied to real-world hard drive data from Backblaze. By evaluating the performance of each brand across four lifetime metrics， this study provides a valuable quantitative basis for enterprises to optimize online quality control and future procurement decisions.
