| dc.contributor.advisor | 薛慧敏 | zh_TW |
| dc.contributor.advisor | Hsueh, Huey Miin | en_US |
| dc.contributor.author (作者) | 劉明得 | zh_TW |
| dc.contributor.author (作者) | Liu, Mingte | en_US |
| dc.creator (作者) | 劉明得 | zh_TW |
| dc.creator (作者) | Liu, Mingte | en_US |
| dc.date (日期) | 2009 | en_US |
| dc.date.accessioned | 11-十月-2011 16:50:04 (UTC+8) | - |
| dc.date.available | 11-十月-2011 16:50:04 (UTC+8) | - |
| dc.date.issued (上傳時間) | 11-十月-2011 16:50:04 (UTC+8) | - |
| dc.identifier (其他 識別碼) | G0923545041 | en_US |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/51556 | - |
| dc.description (描述) | 博士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 統計研究所 | zh_TW |
| dc.description (描述) | 92354504 | zh_TW |
| dc.description (描述) | 98 | zh_TW |
| dc.description.abstract (摘要) | 泊松分佈(Poisson distribution)是一經常被配適於稀有事件建模的機率分配，其應用領域相當的廣泛，如生物，商業，品質控制等。其中許多的應用均為兩群體均數的比較，如欲檢測一新的處理是否較原本的處理俱優越性(superiority)，或者欲驗證一新的方法相較於舊的方法是否俱有不劣性(non-inferiority)。因此，此研究的目標為發展假設檢定的方法，用於比較兩獨立的泊松樣本是否有優越性及不劣性。一般探討假設檢定方法時，均因干擾參數的出現而導致理論探討及計算上的困難。為因應此困境，本研究由簡入繁，亦即先探討相等式的虛無假設(the null hypothesis of equality)，繼而，再推展至非優越性的虛無假設(the null hypothesis of non-superiority)，最後將這些探究的假設檢定方法應用至檢定不劣性並驗證這些方法的適用性。 兩種Wald 檢定統計量是本研究主要的研究興趣。對應於這兩種檢定統計量的近似的假設檢定法，是利用其極限分配為常態分配的特性而衍生的。此研究裡，可推導得到近似的檢定法的檢定力函數及欲達成某一檢定力水平時所需的樣本數公式。並依據此檢定力函數檢驗此檢定法的有效性(validity)及不偏性(unbiasedness)。並且推廣一連續修正的方法至任何的樣本數組合。另外一方面，此研究亦介紹並推廣兩種p-值的正確(exact)檢定法。其中一種為信賴區間p值檢定法(Berger和Boss, 1994), 另一種為估計的p值檢定法(Krishnamoorthy和Thomson, 2004)。一般正確檢定法較需要繁瑣的計算，故此研究將提出某些步驟以降低計算的負擔。就信賴區間p值檢定法而言，其首要工作為縮減求算p值的範圍，並驗證所使用的檢定統計量是否滿足Barnard凸面(convexity)的條件。若此統計量符合凸面convexity的條件，且在Poisson 的問題上，則此正確的信賴區間p值將出現在屬於虛無假設的參數空間的邊界上。然而，對於估計的p值檢定法而言，因在虛無假設的參數空間上求得Poisson均數的最大概似估計值，並不簡單及無法直接求得，故在此研就，將以一Poisson均數的點估計值代替。對於正確的假設檢定方法，此研究亦提出一個欲達成某一檢定力水平時所需的樣本數的步驟。 此研究將透過一個廣大的數值分析來驗證之前所提出的假設檢定方法。其中，可發現這些近似的假設檢定法之間的差異會受到兩群體之樣本數之比率的影響，而連續性的修正於某些情況下確實能夠使型I誤差較能夠受到控制。另外，當樣本數不夠多時，正確的假設檢定法是較近似的假設檢定法適當，尤其在型I誤差的控制上更是明顯。最後，此研究所提出的假設檢定方法將實際應用於一組乳癌治療的資料。 | zh_TW |
| dc.description.abstract (摘要) | The Poisson distribution is a well-known suitable modelfor modeling a rare events in variety fields such as biology,commerce, quality control, and so on. Many applications involvecomparisons of two treatment groups and focus on showing thesuperiority of the new treatment to the conventional one, or thenon-inferiority of the experimental implement to the standardimplement upon the cost consideration. We aim to develop statisticaltests for testing the superiority and non-inferiority by twoindependent random samples from Poisson distributions. In developingthese tests, both computational and theoretical difficulties arisefrom presence of nuisance parameters. In this study, we firstconsider the problems with the null hypothesis of equality forsimplicity. The problems are extended to have a regular nullhypothesis of non-superiority next. Subsequently, the proposedmethods are further investigated in establishing thenon-inferiority.Two types of Wald test statistics are of our main research interest.The correspondent asymptotic testing procedures are developed byusing the normal limiting distribution. In our study, the asymptoticdistribution of the test statistics are derived. The asymptoticpower functions and the sample size formula are further obtained.Given the power functions, we justify the validity and unbiasednessof the tests. The adequate continuity correction term for thesetests is also found to reduce inflation of the type I error rate. Onthe other hand, the exact testing procedures based on two exact$p$-values, the confidence-interval $p$-value (Berger and Boos(1994)), and the estimated $p$-value (Krishnamoorthy and Thomson(2004)), are also applied in our study. It is known that an exacttesting procedure tends to involve complex computations. In thisthesis, several strategies are proposed to lessen the computationalburden. For the confidence-interval $p$-value, a truncatedconfidence set is used to narrow the area for finding the $p$-value.Further, the test statistic is verify whether they fulfill theproperty of convexity. It is shown that under the convexity theexact $p$-value occurs somewhere of the boundary of the nullparameter space. On the other hand, for the estimated $p$-value, asimpler point estimate is applied instead of the use of therestricted maximum likelihood estimators, which are lessstraightforward in this problem. The estimated $p$-value is shown toprovide a conservative conclusion. The calculations of the samplesizes required by using the two exact tests are discussed.Intensive numerical studies show that the performances of theasymptotic tests depend on the fraction of the two sample sizes andthe continuity correction can be useful in some cases to reduce theinflation of the type I error rate. However, with small samples, thetwo exact tests are more adequate in the sense of having awell-controlled type I error rate. A data set of breast cancerpatients is analyzed by the proposed methods for illustration. | en_US |
| dc.description.tableofcontents | 1. Introduction 1 1.1 Motivation 1 1.2 Outline 62. Testing the null hypothesis of equality 7 2.1 Statistical Hypothesis and Test Statistics 8 2.2 Asymptotic $p$-values 9 2.3 Exact $p$-values 15 2.4 Numerical study 173. Testing the superiority 33 3.1 Statistical hypothesis and Test Statistics 33 3.2 Asymptotic $p$-values 34 3.3 Exact $p$-values 37 3.4 Numerical study 424. Testing the non-inferiority 47 4.1 Statistical hypothesis and Test Statistics 48 4.2 Asymptotic $p$-values 49 4.3 Exact $p$-values 54 4.4 Numerical study 595. Real example 756. Concluding remarks 777. Reference 798. Appendix 81 | zh_TW |
| dc.language.iso | en_US | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0923545041 | en_US |
| dc.subject (關鍵詞) | 近似的假設檢定法 | zh_TW |
| dc.subject (關鍵詞) | Barnard凸面 | zh_TW |
| dc.subject (關鍵詞) | 正確的假設檢定法 | zh_TW |
| dc.subject (關鍵詞) | 不偏性 | zh_TW |
| dc.subject (關鍵詞) | Asymptotic test | en_US |
| dc.subject (關鍵詞) | Barnard convexity condition | en_US |
| dc.subject (關鍵詞) | exact test | en_US |
| dc.title (題名) | 二獨立卜瓦松均數之比較 | zh_TW |
| dc.title (題名) | Superiority or non-inferiority testing procedures for two independent poisson samples | en_US |
| dc.type (資料類型) | thesis | en |
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