Please use this identifier to cite or link to this item:
https://ah.lib.nccu.edu.tw/handle/140.119/88738
DC Field | Value | Language |
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dc.contributor.advisor | 宋傳欽 | zh_TW |
dc.contributor.advisor | Song, Chwan Chin | en_US |
dc.contributor.author | 謝季英 | zh_TW |
dc.contributor.author | Shieh, Jih Ing | en_US |
dc.creator | 謝季英 | zh_TW |
dc.creator | Shieh, Jih Ing | en_US |
dc.date | 1994 | en_US |
dc.date.accessioned | 2016-04-29T08:32:25Z | - |
dc.date.available | 2016-04-29T08:32:25Z | - |
dc.date.issued | 2016-04-29T08:32:25Z | - |
dc.identifier | B2002003906 | en_US |
dc.identifier.uri | http://nccur.lib.nccu.edu.tw/handle/140.119/88738 | - |
dc.description | 碩士 | zh_TW |
dc.description | 國立政治大學 | zh_TW |
dc.description | 應用數學系 | zh_TW |
dc.description | 81155009 | zh_TW |
dc.description.abstract | 在線性迴歸分析中,資料的不適當,常導致研究者選擇了不當的模式,為避免此缺失,在分析資料前須先做好診斷工作。本文中將從貝氏觀點提出一些不同的診斷方法以供參考。首先推導出均數移動參數a=(a<sub>1</sub>,…,a<sub>k</sub>)`的事後分配,並利用a`a/k的事後均數診斷出不當資料點。接著,考慮在個別模式下以β事後分配之總變異及廣義變異為標準,診斷出離群值及具有潛在影響力之觀測值。最後,分別利用(i)β的事後分配(ii)σ<sup>2</sup>的事後分配(iii)(β,σ<sup>2</sup>)的聯合事後分配,推導出對應的對稱均方差以做為診斷標準。 | zh_TW |
dc.description.abstract | In this thesis, some different diagnostic methodologies for outliers and influential observations from Bayesian point of view are proposed. We firstly derive the marginal posterior distribution of the mean-shift parameter a=(a<sub>1</sub>,a<sub>k</sub>)<sup>1</sup>, then use the posterior mean of a<sup>1</sup>a/k to detect the spurious data items. Secondly, we use the posterior total variance and generalized variance of β as diagnostic criterions for outliers and influential observations. Finally, we utilize (i) the posterior distribution of β, (ii) the posterior distribution of σ<sup>2</sup>, and (iii) the joint posterior distribution of β, σ<sup>2</sup> to find their corresponding symmetric mean square differences , which can be used as diagnostic criterions. | en_US |
dc.description.tableofcontents | 摘要\r\n目錄-----i\r\n第一章 緒論-----1\r\n 1.1 前言-----1\r\n 1.2 本文架構-----2\r\n 1.3 文獻回顧-----2\r\n 1.3.1 傳統診斷-----2\r\n 1.3.2 貝氏診斷-----4\r\n第二章 模式中參數之事後分配-----7\r\n 2.1 均數移動不當模式的簡介-----7\r\n 2.2 參數(α,β,σ<sup>2</sup>)之事前及事後分配-----8\r\n 2.3 參數β之事後分配-----9\r\n 2.4 參數σ<sup>2</sup>之事後分配-----11\r\n 2.5 參數α之事後分配-----12\r\n第三章 離群值及具有影響力觀測值之診斷-----17\r\n 3.1 診斷方法之回顧-----17\r\n 3.1.1 以β事後分配的權數為診斷標準-----17\r\n 3.1.2 以β事後分配之總變異為診斷標準-----18\r\n 3.1.3 以Kullback-Leibler對稱散度為診斷標準-----19\r\n 3.2 其它診斷法-----23\r\n 3.2.1 以α`α/k事後均數為診斷標準-----23\r\n 3.2.2 在個別模式下以β事後分配的總變異及廣義變異為診斷標準-----25\r\n 3.2.3 以對稱均方差為診斷標準-----27\r\n第四章 實例分析-----38\r\n 4.1 資料描述-----38\r\n 4.2 資料分析-----39\r\n 4.3 結論-----45\r\n附錄-----52\r\n參考文獻-----68 | zh_TW |
dc.source.uri | http://thesis.lib.nccu.edu.tw/record/#B2002003906 | en_US |
dc.subject | 貝氏 | zh_TW |
dc.subject | 離群值 | zh_TW |
dc.subject | 影響力之觀測值 | zh_TW |
dc.subject | 不正當 | zh_TW |
dc.subject | 均數移動 | zh_TW |
dc.subject | 對稱均方差 | zh_TW |
dc.subject | Bayesian | en_US |
dc.subject | outliers | en_US |
dc.subject | influential observations | en_US |
dc.subject | spurious | en_US |
dc.subject | mean- shift | en_US |
dc.subject | symmetric mean square difference | en_US |
dc.title | 從貝氏觀點診斷離群值及具有影響力之觀察值 | zh_TW |
dc.title | Some diagnostics for outliers and influential observations from Bayesian point of view | en_US |
dc.type | thesis | en_US |
item.cerifentitytype | Publications | - |
item.grantfulltext | open | - |
item.openairetype | thesis | - |
item.fulltext | With Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_46ec | - |
Appears in Collections: | 學位論文 |
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