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A note on time-ordered classification

机译:关于按时间顺序分类的注释

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摘要

Department of Mathematics, University of Kansas, Lawrence, Kansas 66045-7523, U.S.A.nhhe@math.ku.edunLemma 2 in §4 of Hinkley (1972) shows that ξn† − ξn∗ = Op(1) under the condition:nsupnu0003θ1nξnu0002∗+nni=ξ ∗+1n{log f1(Xi ; θ1) − log f2(Xi ; θn∗n2 )} → −∞ (1)nwith probability 1 as n→∞, which was described to be from the ‘consistency assumptions’. Herenθ1, θ2, f1, f2, log f1(Xi ; θ1) and log f2(Xi ; θn∗n2 ) are used to replace respectively θ,ψ, f, g, l fi (θ) andnlgi (ψn∗) in Hinkley (1972). Condition (1) fails whenever the distribution corresponding to the densitynf2(·; θn∗n2 ) is in the closure of the set of distributions corresponding to densities of the form f1(·; θ1), inna certain sense. One such case occurs if f1, f2 have the same parametric form with parameters θn∗n1 , θn∗n2 ,nrespectively, satisfying θn∗n1nu0006= θn∗n2 . Another such case occurs when the distribution with density f2(·; θn∗n2 ) cannbe viewed as a limit of the distributions with densities f1(·; θ1). For instance, an exponential distributionncan be viewed as a limit of a Weibull distribution. Thus, the ‘consistency conditions’ in Hinkley (1972)nmay not be satisfied by otherwise well-behaved models. Here we give consistency assumptions to remedynthis.nLet us define v(θ j ; θn∗ni ) ≡nu0003 +∞n−∞ {log f j (x; θ j ) − log fi (x; θn∗ni )} fi (x; θn∗ni ) dx for i, j = 1, 2.nAssumption 1. We have f1(x; θn∗n1 ) u0006= f2(x; θn∗n2 ) on a set of nonzero measure.nAssumption 2. We have θ j and θn∗nj are in u0003θ j , where u0003θ j is compact ( j = 1, 2).nAssumption 3.n1. For any j, s, t satisfying j = 1, 2, 0u0002s < t u0002T , there exist r < 2,C > 0 such thatnEn⎧⎨n⎩ maxnθ j∈u0003θ jnu0007 u0002tni=s+1n[log f j (Xi ; θ j ) − E{log f j (Xi ; θ j )}]nb2n⎫⎬n⎭ u0002C(t − s)r .n2. For any j, s1, s2, t1, t2 satisfying j = 1, 2, 0u0002s1 < t1 u0002ξn∗, ξn∗ u0002s2 < t2 u0002T , there exist r u00021, D > 0nsuch thatnEn⎧⎪⎨n⎪⎩nmaxnθ j∈u0003θ jn⎛n⎝nu0002t jni=s j+1n|v(θ j ; θn∗nj )|−12n[{log f j (Xi ; θ j ) − log f j (Xi ; θn∗nj )} − v(θ j ; θn∗nj )]n⎞n⎠n2n⎫⎪⎬n⎪⎭nu0002 D(t − s)r .nIt is easy to verify that an exponential family satisfies the relatively weak Assumption 3.nUnder Assumptions 1–3, it can be proved that ξn† − ξn∗ = Op(1). The author may be contacted for anproof.nREFERENCEnHINKLEY, D. V. (1972). Time-ordered classification. Biometrika 59, 509–23.n[Received November 2007. Revised July 2008
机译:堪萨斯大学数学系,劳伦斯,堪萨斯州66045-7523,USAnhhe@math.ku.edun Hinkley(1972)§4中的引理2显示ξn†−ξn∗ = Op(1)在以下条件下:nsupnu0003θ1nξnu0002∗ + nni =ξ∗ + 1n {log f1(Xi;θ1)− log f2(Xi;θn∗ n2)}→-∞(1)n,概率1为n→∞,这被描述为来自“一致性假设” '。在这里,θ1,θ2,f1,f2,log f1(Xi;θ1)和log f2(Xi;θn∗ n2)分别用于替换欣克利的θ,ψ,f,g,l fi(θ)和nlgi(ψn∗) (1972)。只要在某种意义上,只要与密度nf2(·;θn* n2)相对应的分布处于与形式为f1(·;θ1)的密度相对应的一组分布的封闭中,条件(1)就会失效。如果f1,f2分别具有相同的参数形式且参数θn* n1,θn* n2满足θn* n1nu0006 =θn* n2,则会发生这种情况。当不能将具有密度f2(·;θn* n2)的分布视为密度f1(·;θ1)的分布的极限时,会发生另一种这种情况。例如,指数分布n可以看作是威布尔分布的极限。因此,本来表现良好的模型可能无法满足Hinkley(1972)中的“一致性条件”。这里我们给出一致性的假设来补救。n让我们定义v(θj;θn∗ ni)≡nu0003+ ∞n−∞ {log fj(x;θj)− log fi(x;θn∗ ni)} fi(x ;对于i,j = 1,2.n的θn∗ ni)dx。在一组非零度量上,我们有f1(x;θn∗ n1)u0006 = f2(x;θn∗ n2)。n假设2。 θj和θn∗ nj在u0003θj中,其中u0003θj是紧凑的(j = 1,2).n假设3.n1。对于任何满足j = 1,2,0u0002s 0使得nEn⎧⎨n⎩maxnθj∈u0003θjnu0007 u0002tni = s + 1n [log fj(Xi; θj)-E {log fj(Xi;θj)}]nb2n⎫⎬n⎭u0002C(t-s)r .n2。对于满足j = 1,2,2,0u0002s1 0n使得nEn⎧⎪⎨n⎪⎩nmaxnθj∈u0003θ jn⎛n⎝nu0002tjni = s j + 1n | v(θj;θn∗ nj)| −12n [{log fj(Xi;θj)− log fj(Xi;θn∗ nj)} − v(θj ;θn* nj)]n⎞n⎠n2n⎫⎪⎬n⎪⎭nu0002D(t-s)r .n很容易验证指数族满足相对弱的假设3。在假设1-3下,它可以证明ξn†−ξn∗ = Op(1)。可以联系作者寻求证据。nREFERENCEnHINKLEY,D. V.(1972)。按时间顺序分类。 Biometrika 59,509-23.n [2007年11月接收。2008年7月修订

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  • 来源
    《Biometrika》 |2009年第1期|p.248-248|共1页
  • 作者

    H. He;

  • 作者单位

    Department of Mathematics, University of Kansas, Lawrence, Kansas 66045-7523, U.S.A. hhe@math.ku.edu;

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