简介：Linearmixedmodels(LMMs)havebecomeanimportantstatisticalmethodforanalyzingclusterorlongitudinaldata.Inmostcases,itisassumedthatthedistributionsoftherandomeffectsandtheerrorsarenormal.Thispaperremovesthisrestrictionsandreplacethembythemomentconditions.WeshowthattheleastsquareestimatorsoffixedeffectsareconsistentandasymptoticallynormalingeneralLMMs.Aclosed-formestimatorofthecovariancematrixfortherandomeffectisconstructedanditsconsistentisshown.Basedonthis,theconsistentestimatefortheerrorvarianceisalsoobtained.Asimulationstudyandarealdataanalysisshowthattheprocedureiseffective.

简介：Receiveroperatingcharacteristic(ROC)curvesareoftenusedtostudythetwosampleprobleminmedicalstudies.However,mostdatainmedicalstudiesarecensored.UsuallyanaturalestimatorisbasedontheKaplan-Meierestimator.InthispaperweproposeasmoothedestimatorbasedonkerneltechniquesfortheROCcurvewithcensoreddata.Thelargesamplepropertiesofthesmoothedestimatorareestablished.Moreover,deficiencyisconsideredinordertocomparetheproposedsmoothedestimatoroftheROCcurvewiththeempiricalonebasedonKaplan-Meierestimator.ItisshownthatthesmoothedestimatoroutperformsthedirectempiricalestimatorbasedontheKaplan-Meierestimatorunderthecriterionofdeficiency.Asimulationstudyisalsoconductedandarealdataisanalyzed.

简介：Totacklemulticollinearityorill-conditioneddesignmatricesinlinearmodels,adaptivebiasedestimatorssuchasthetime-honoredSteinestimator,theridgeandtheprincipalcomponentestimatorshavebeenstudiedintensively.Tostudywhenabiasedestimatoruniformlyoutperformstheleastsquaresestimator,somesufficientconditionsareproposedintheliterature.Inthispaper,weproposeaunifiedframeworktoformulateaclassofadaptivebiasedestimators.Thisclassincludesallexistingbiasedestimatorsandsomenewones.Asufficientconditionforoutperformingtheleastsquaresestimatorisproposed.Intermsofselectingparametersinthecondition,wecanobtainalldouble-typeconditionsintheliterature.

简介：§1.IntroductionandMainResultLet(X,F)beaJBrXR'-valuedvector.AssumethatwhenX=xisgiven,thereexistsaconditionaldensityofYtobedenotedbyf(y[x),whichisaBorel-measurablefunctionof(x,y).Notethatwedonotassumetheexistenceofadensityfunctionof(X,F).Let(X-i,fi),—,(Xn,Fn)bei.i.d.samplesof(X,F).Ourpurposeistoestimatef(y\x)basedonthesesamples.Thisisaninterestingprobleminviewofeitherpuretheoryorpracticalapplications.MotivatedbytheideasuggestedinkernelandNNestimationsinthetheoryofnonparametricregressionanddensityestimates,thefirstauthorproposesthefollowingtwoclassesofestimatorsoff(y\x):

简介：

简介：

简介：三个最佳的线性态度评估者用一条几何途径为太空船态度的单个点的即时评价被建议。最后的最佳的态度被修改Rodrigues参数(MRP)代表。在为每测量价值介绍偶然的右边的直角的坐标以后，三错误向量被点或/并且生气产品的使用获得。相应optimality标准严厉地二次、非强迫，它不与Wahbas与抑制标准一致。奇特，当主要角度接近时，发生，能被一合适的旋转容易避免。数字模拟证明建议三个最佳的线性评估者能提供与遵循Wahbaoptimality定义的那些可比较的精确，并且比著名四元数评估者(探索)有更快的计算速度。

简介：Thispaperisconcernedwiththeoptimallinearestimationproblemforlineardiscrete-timestochasticsystemswithrandommeasurementdelays.Anewmodelthatdescribestherandomdelaysisconstructedwherepossiblethelargestdelayisbounded.Basedonthisnewmodel,theoptimallinearestimatorsincludingfilter,predictorandsmootheraredevelopedviaaninnovationanalysisapproach.TheestimatorsarerecursivelycomputedintermsofthesolutionsofaRiccatidifferenceequationandaLyapunovdif...

简介：在这份报纸，有在风险上并且随着时间的过去的依赖结构的一种类型的多维的可靠模型被考虑。借助于设计方法，不同类、同类的B

简介：让fn基于内核函数K和在d维的单位范围Sd拿值的独立、相等分布式的随机的变量的一个序列是方向性的数据的非参量的内核密度评估者?1。如果核功能是有围住的变化的功能，随机的变量的密度功能f是连续的，它被证明那，那么大的偏差原则和中等偏差原则为成立。关键词核密度评估者-方向性的数据-中等偏差-大偏差先生(2000)题目分类60F10-62F12由中国(资助号码10571139)的国家自然科学基础支持了

简介：在这份报纸，当错误形成一个严格地静止的混合序列时，为在一个线性模型的回归参数的M评估者的中等偏差被获得。结果被使用学习象Hubers评估者，Lp回归评估者，最少的广场评估者和最不绝对的偏差评估者那样的M评估者的许多不同类型。

简介：Stronguniformconsistencyratesaregivenforkerneltypeestimatorsoftheconditionalfunctionwith（?）-mixingsample.Especially,fornonparametricestimatorsofkerneldensity,theregressionfunctionwhenYisbounded,conditionaldf’s,L-smoothingandM-smoothing,weobtainthesamerateO(（n/logn）-1/3)asinthei.i.d.sampleestablishedbyH（?）rdle,JanssenandSerfling.

简介：TwoclassesofMallowsGM-estimatorswithinvarianceareconsideredinthestochasticlinearregressionmodel.Someoftheirasymptoticpropertiesaredescribed,andthefittedvalueinfluenceandvariancecomponentsarecomparedbymeansofrobustcovariances,

简介：这篇文章在生长曲线模型题目为回归系数考虑线性评估者的考虑到不完全的椭圆体的限制。为分别地，在同类、非同类的线性评估者的班上可被考虑的线性评估者的必要、足够的条件在二次的损失功能下面被获得。他们是在文学的一些存在结果的归纳。

简介：Thispaperconsiderstheconvergenceratesfornonparametricestimatorsoftheerrordistributioninsemi-parametricregressionmodels.Byestablishingsomegenerallawsoftheiteratedlogarithm,itshowsthattheratesofconvergenceofeithertheempiricaldistributionorasmoothedversionoftheempiricaldistributionfunctionmatchesexactlytheratesobtainedforanindependentsamplefromtheerrordistribution.

简介：ForpartiallinearmodelY=X^Tβ0+go(T)+εwithunknownβ∈R^dandanunknownsmoothfunctiongo，thispaperconsiderstheHuber-Dutterestimatorsofβ0,scaleσfortheerrorsandthefunctiong0respectively,inwhichthesmoothingB-splinefunctionisused.Undersomeregularconditions,itisshownthattheHuber-Dutterestimatorsofβ0andσareasymptoticallynormalwithconvergenceraten^-1/2andtheB-splineHuber-Dutterestimatorofg0achievestheoptimalconvergencerateinnonparametricregression.AsimulationstudydemonstratesthattheHuber-Dutterestimatorofβ0iscompetitivewithitsM-estimatorwithoutscaleparameterandtheordinaryleastsquareestimator.Anexampleispresentedafterthesimulationstudy.

简介：Higher-orderalmostcyclostationarycomplexprocessesarecomplexrandomsignalswithalmostperiodicallytime-varyingstatistics,whichisimportanttotheresearchofnon-Gaussiansignalsininformationsystem.Intinspaper,smoothedpolyperiodogramsareproposedforrelatedtocyclicpolyspectralestimationandareshowntobeconsistentandasymptoticallycomplexnormal.Asymptoticcovarianceexpressionsarederivedalongwiththeircomputableforms.

简介：Inthisarticle,apartiallylinearsingle-indexmodelforlongitudinaldataisinvestigated.Thegeneralizedpenalizedsplineleastsquaresestimatesoftheunknownparametersaresuggested.Allparameterscanbeestimatedsimultaneouslybytheproposedmethodwhilethefeatureoflongitudinaldataisconsidered.Theexistence,strongconsistencyandasymptoticnormalityoftheestimatorsareprovedundersuitableconditions.Asimulationstudyisconductedtoinvestigatethefinitesampleperformanceoftheproposedmethod.Ourapproachcanalsobeusedtostudythepuresingle-indexmodelforlongitudinaldata.

简介：

简介：这篇文章涉及部分线性的回归建模的semiparametric变化系数的估计的问题。由联合，本地多项式和最少的广场过程扇子和黄(2005)建议侧面最不为参量的部件摆平评估者并且建立了它的asymptotic规度。我们进一步证明侧面最不摆平评估者能完成重申的对数的法律。而且，我们学习描绘非线性的部分以及错误变化的功能的评估者。强壮的集中率和重申的对数的法律分别地为他们被导出。