【摘要】：文章以航天飛機在不同溫度下發(fā)射密封圈的失效數據為例,采用隨機游動與變量變換M-H算法獲得Logistic回歸模型參數的后驗分布樣本并進行貝葉斯分析。同時,進行蒙特卡洛模擬,通過樣本軌跡圖、直方圖、自相關系數圖等考查M-H算法的抽樣表現,并討論每種抽樣方法的優(yōu)缺點與提高措施。結果表明:先驗分布的選取直接影響貝葉斯估計效果,有先驗信息的M-H算法估計的標準差比無先驗信息的M-H算法要精確,但隨著樣本容量增大,趨勢在減少,適當的建議分布與變量變換可大大提高M-H算法的抽樣效率。
[Abstract]:In this paper, taking the failure data of the space shuttle's launching sealing ring at different temperatures as an example, the posterior distribution samples of the parameters of the Logistic regression model are obtained by using the M-H algorithm of random walk and variable transformation, and the Bayesian analysis is carried out. At the same time, Monte Carlo simulation is carried out. The sampling performance of M-H algorithm is examined by sample locus, histogram and autocorrelation coefficient diagram, and the advantages and disadvantages of each sampling method and the improvement measures are discussed. The results show that the selection of prior distribution directly affects the effect of Bayesian estimation. The standard deviation of M-H algorithm with prior information is more accurate than that of M-H algorithm without prior information, but the trend decreases with the increase of sample size. The sampling efficiency of M-H algorithm can be greatly improved by appropriate recommended distribution and variable transformation.
【作者單位】： 天水師范學院數學與統(tǒng)計學院;
【基金】：國家自然科學基金資助項目(61104045) 天水師范學院中青年教師科研資助項目(TSA1506)
【分類號】：O212.8

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