版權(quán)說(shuō)明:本文檔由用戶(hù)提供并上傳,收益歸屬內(nèi)容提供方,若內(nèi)容存在侵權(quán),請(qǐng)進(jìn)行舉報(bào)或認(rèn)領(lǐng)
文檔簡(jiǎn)介
1、ConvergenceRatesOfNearestNeighborDensityEstimatorsunderNASampleGrade:2013Major:ProbabilityandStatisticsResearchfie]d:StatisticsGraduate:ZhuTiantianSupervisor:QinYongsongABSTRACTTheconceptofnegativelyassociated(NA)sequenc
2、ewasproposedfirstlybyJoag—DeyandProschan(1983)andBlockandSavits(1982)Joag—DeyandProschandiscussedsomebasicprop—ertiesandpracticalapplicationsofNAsequencesRoussas(1994)andBeak(2003)alsostudiedthebasicpropertiesofNAsequenc
3、essuchascompleteconvergenceDuetothebroadapplicationsofNAsequences,thestatisticalinferencesassociatedwithNAsequenceshaveattractedgreatatten—tionsofmanyscholarsLargesamplepropertiesofNAsequencessuchastheasymptoticnormality
4、ofkerneldensityestimationhavebeeninvestigatedextensivelyNearestneighbordensityestimatorisanimportantnon—parametricdensityestimationmethod,whichwasproposedbyLoflsgardenandQuesenberryin1965Theyprovedtheweakconsistencyofnea
5、restneighbordensityestimator厶(z)Sincetheconceptofnearestneighbordensityesti—matorwasintroduced,manyscholarsstudieditscharacteristicssuchasconsistencyanduniformconvergenceunderallkindsofsamplesandvariousconditionsAsaresul
6、t,alotofgoodresultshavebeenobtainedinthisaspectAccordingtotheideaofnearestneighborestimator,Yuproposedanewnearestneighbordensityestimator厶(z)basedonorderstatisticsin1986Underindependentsamplesandmildconditions,heestablis
7、heditspointwiseweakandstrongconsistencyuniformweakandstrongconsistencyandtheL1一moldstrongconsistencyonaboundedintervalXue(1992,1994)con—sideredtheconsistencyandconvergenceratesof厶(z)underindependentand妒一mixingsamplesresp
8、ectivelyThispapermainlystudiesthepointwiseconsistencyandstronguniformconsistencyandobtaintheconvergenceratesofpointwiseconsistencyanduniformstrongconsistencyofin(z)respectivelyHerewesummarythemainlynewfindingsandinnovati
9、onsinthispaper:1Thispaperfirstlyprovestheconsistencyofnearestneighbordensityestimator厶(z)andobtainstheconvergenceratesunderNAsample2Themethodfromthispapercanprovideagoodreferenceforstudyinglargesamplechar—acteristicsof厶(
溫馨提示
- 1. 本站所有資源如無(wú)特殊說(shuō)明,都需要本地電腦安裝OFFICE2007和PDF閱讀器。圖紙軟件為CAD,CAXA,PROE,UG,SolidWorks等.壓縮文件請(qǐng)下載最新的WinRAR軟件解壓。
- 2. 本站的文檔不包含任何第三方提供的附件圖紙等,如果需要附件,請(qǐng)聯(lián)系上傳者。文件的所有權(quán)益歸上傳用戶(hù)所有。
- 3. 本站RAR壓縮包中若帶圖紙,網(wǎng)頁(yè)內(nèi)容里面會(huì)有圖紙預(yù)覽,若沒(méi)有圖紙預(yù)覽就沒(méi)有圖紙。
- 4. 未經(jīng)權(quán)益所有人同意不得將文件中的內(nèi)容挪作商業(yè)或盈利用途。
- 5. 眾賞文庫(kù)僅提供信息存儲(chǔ)空間,僅對(duì)用戶(hù)上傳內(nèi)容的表現(xiàn)方式做保護(hù)處理,對(duì)用戶(hù)上傳分享的文檔內(nèi)容本身不做任何修改或編輯,并不能對(duì)任何下載內(nèi)容負(fù)責(zé)。
- 6. 下載文件中如有侵權(quán)或不適當(dāng)內(nèi)容,請(qǐng)與我們聯(lián)系,我們立即糾正。
- 7. 本站不保證下載資源的準(zhǔn)確性、安全性和完整性, 同時(shí)也不承擔(dān)用戶(hù)因使用這些下載資源對(duì)自己和他人造成任何形式的傷害或損失。
最新文檔
- 12701.正相協(xié)樣本下最近鄰密度估計(jì)的收斂速度
- ND樣本最近鄰密度估計(jì)的相合性.pdf
- ND序列最近鄰密度估計(jì)的相合速度.pdf
- 非參數(shù)回歸誤差密度估計(jì)的收斂速度.pdf
- 相依樣本小波密度估計(jì)的漸近性質(zhì).pdf
- 27030.刪失數(shù)據(jù)下核密度估計(jì)的收斂性質(zhì)
- 人群的密度估計(jì)與運(yùn)動(dòng)估計(jì).pdf
- 強(qiáng)混合樣本下邊緣頻率插值密度估計(jì)的漸近性質(zhì).pdf
- 人群密度估計(jì)的算法研究.pdf
- 一般形式的密度估計(jì).pdf
- 基于核密度估計(jì)的圖像分割方法
- 基于非參數(shù)密度估計(jì)點(diǎn)樣本分析建模的應(yīng)用研究.pdf
- 基于人群密度估計(jì)的視頻監(jiān)控技術(shù).pdf
- 基于視頻的人群密度估計(jì)研究.pdf
- 最近鄰查詢(xún)和反最近鄰查詢(xún)算法研究.pdf
- 基于核密度估計(jì)的圖像分割方法.pdf
- 基于紋理分析的人群密度估計(jì).pdf
- 基于視頻圖像的人流密度估計(jì).pdf
- 視頻監(jiān)控中人群密度估計(jì)研究.pdf
- Copula選擇及其條件核密度估計(jì).pdf
評(píng)論
0/150
提交評(píng)論