Hess and Brown(1999) 回顧多種對於右設限數據之風險率的核估計方法，且經由模擬比較發現，Muller and Wang(1994) 所提出的邊界核估計法的估計效果較好。本文之目的是在貝氏模型下，提出對於右設限數據之風險率的估計。在這貝氏方法中，我們利用 Bernstein 多項式來表達累積風險率，而將先驗分佈建立在這些 Bernstein 多項式的次數及係數上；統計推論所需之後驗分佈是利用 MCMC 的方法來做。最後，我們把我們的方法與 Muller and Wang(1994) 的邊界核估計法做模擬比較，結果顯示我們的貝氏方法有較小的均方誤差。 Hess and Brown(1999) reviewed various kernel methods for hazard rate estimation based on right-censored data. Through simulations, they found that the boundary kernel estimator by Muller and Wang(1994) had improved performance. In this paper, we will propose a Bayesian estimator for hazard rate, using prior on Bernstein polynomials, and make inference using MCMC methods. Comparison using simulation shows that our Bayesian estimator performs better than the boundary kernel estimator of Muller and Wang(1994) in terms of mean-squared error.
