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作者：Phillips, PCB; Shimotsu, K
作者单位：Yale University; Queens University - Canada
摘要：Asymptotic properties of the local Whittle estimator in the nonstationary case (d > 1/2) are explored. For 1/2 < d less than or equal to 1, the estimator is shown to be consistent, and its limit distribution and the rate of convergence depend on the value of d. For d = 1, the limit distribution is mixed normal. For d > 1 and when the process has a polynomial trend of order alpha > 1/2, the estimator is shown to be inconsistent and to converge in probability to unity.
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作者：Hallin, M; Lu, ZD; Tran, LT
作者单位：Universite Libre de Bruxelles; Chinese Academy of Sciences; University of London; London School Economics & Political Science; Indiana University System; Indiana University Bloomington
摘要：A local linear kernel estimator of the regression function x --> g(x) := E[Y-i\X-i = x], X is an element of R-d, of a stationary (d + 1)-dimensional spatial process {(Y-i, X-i), i is an element of Z(N)} observed over a rectangular domain of the form l(n) := {i = (i(1),..., i(N)) is an element of Z(N)\ 1 less than or equal to i(k) less than or equal to n(k), k = 1,..., N}, n = (n(1),..., n(N)) is an element of Z(N), is proposed and investigated. Under mild regularity assumptions, asymptotic nor...
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作者：Nieto-Barajas, LE; Prünster, I; Walker, SG
作者单位：University of Pavia; University of Kent
摘要：This paper introduces and studies a new class of nonparametric prior distributions. Random probability distribution functions are constructed via normalization of random measures driven by increasing additive processes. In particular, we present results for the distribution of means under both prior and posterior conditions and, via the use of strategic latent variables, undertake a full Bayesian analysis. Our class of priors includes the well-known and widely used mixture of a Dirichlet proce...
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作者：Fan, JQ; Zhang, J
作者单位：Princeton University; Chinese University of Hong Kong; University of Kent; Chinese Academy of Sciences
摘要：Generalized likelihood ratio statistics have been proposed in Fan, Zhang and Zhang [Ann. Statist. 29 (2001) 153-193] as a generally applicable method for testing norparametic hypotheses about nonparametric functions. The likelihood ratio statistics are constructed based on the assumption that the distributions of stochastic errors are in a certain parametric family. We extend their work to the case where the error distribution is completely unspecified via newly proposed sieve empirical likeli...
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作者：Chen, K
作者单位：Hong Kong University of Science & Technology
摘要：Thomas' partial likelihood estimator of regression parameters is widely used in the analysis of nested case-control data with Cox's model. This paper proposes a new estimator of the regression parameters, which is consistent and asymptotically normal. Its asymptotic variance is smaller than that of Thomas' estimator away from the null. Unlike some other existing estimators, the proposed estimator does not rely on any more data than strictly necessary for Thomas' estimator and is easily computa...
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作者：Knight, K
作者单位：University of Toronto
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作者：Weisberg, S
作者单位：University of Minnesota System; University of Minnesota Twin Cities
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作者：Cator, EA
作者单位：Delft University of Technology
摘要：In recent years a popular nonparametric model for coarsened data is an assumption on the coarsening mechanism called coarsening at random (CAR). It has been conjectured in several papers that this assumption cannot be tested by the data, that is, the assumption does not restrict the possible distributions of the data. In this paper we will show that this conjecture is not always true; an example will be current status data. We will also give conditions when the conjecture is true, and in doing...
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作者：Madigan, D; Ridgeway, G
作者单位：Avaya; Rutgers University System; Rutgers University New Brunswick; Rutgers University System; Rutgers University New Brunswick; RAND Corporation; Rand Health
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作者：Breiman, L
作者单位：University of California System; University of California Berkeley
摘要：Tree ensembles are looked at in distribution space, that is, the limit case of infinite sample size. It is shown that the simplest kind of trees is complete in D-dimensional L-2(P) space if the number of terminal nodes T is greater than D. For such trees we show that the AdaBoost algorithm gives an ensemble converging to the Bayes risk.
