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    Title: 基因演算法和差分演算法在逐步移除型一區間設限資料上之可靠度評估應用
    Other Titles: Reliability assessment based on progressively type-I interval censored measurements using genetic algorithm and differential evolution methods
    Authors: 陳宥臻;Chen, Yu-Zhen
    Contributors: 淡江大學統計學系碩士班
    蔡宗儒;Tsai, Tzong-Ru
    Keywords: Burr XII分配;差分演算法;基因演算法;最大概似估計量;逐步型一區間設限;擬牛頓法;Burr type XII distribution;Differential evolution method;Genetic algorithm method;Maximum Likelihood Estimator;Progressively type I interval censoring;Quasi-Newton method
    Date: 2015
    Issue Date: 2016-01-22 14:57:01 (UTC+8)
    Abstract: 基於逐步移除設限法可以允許試驗者收集到較長產品的壽命的優點,以及區間設限法具有容易操作之特性,本論文針對逐步型一區間設限資料,進行模型參數最大參數估計解根方法的穩定性評估。假設產品的壽命服從雙參數的Burr XII分配,分別使用擬牛頓法及啟發式演算法中的基因演算法 (GA) 與差分演算法 (DE) 來求解模型參數的最大概似估計量。藉由統計模擬研究計算參數估計值的偏誤及均方誤,以評估三種不同解根方法的穩定度。研究中發現,GA 及DE在最大參數估計量的解根表現中有較穩定的運算結果。
    To the advantage of progressively censoring method that allow experimenters to observe more extreme lifetimes of products and the administration convenience of the interval censoring method, this thesis evaluates the performance of difference algorithms, the quasi-Newton method, genetic algorithm method and differential evolution methods for maximum likelihood estimates based on the progressively type I interval censored samples for the Burr type XII distribution. An intensive simulation study was conduct for comparing the estimation performance of the quasi-Newton method, genetic algorithm (GA) method and differential evolution (DE) methods. We found that the GA method and DE methods outperform the quasi-Newton method for most cases in the simulation study.
    Appears in Collections:[統計學系暨研究所] 學位論文

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