4期

最新文章

• ON TESTING THE EQUALITY IN DISPERSION OF 2 PROBABILITY-DISTRIBUTIONS AI翻译
  作者：MARZEC, L; MARZEC, P
  摘要：Based on independent random samples from two distributions we consider the problem of testing that the distributions are identical except for an unknown location parameter against the alternative that one is less dispersed than the other. The proposed tests are shown to be asymptotically distribution-free and consistent. The asymptotic relative efficiencies with respect to several other tests for some specific alternatives are given.
• OPTIMAL-DESIGN FOR ESTIMATION OF VARIANCE IN NONPARAMETRIC REGRESSION USING 1ST-ORDER DIFFERENCES AI翻译
  作者：CHAN, LY
  摘要：In nonparametric regression, the variance of the response can be estimated by the sum of squares of differences of the observed response. In this paper we obtain the most efficient design for a general variance estimator defined by first order differences. It is found that for this estimator, in the majority of cases, a good approximation to the most efficient design is the uniform design. The least efficient designs are also discussed.
• MODALITY OF THE RESTRICTED LIKELIHOOD FOR SPATIAL GAUSSIAN RANDOM-FIELDS AI翻译
  作者：DIETRICH, CR
  摘要：Numerical simulations recently reported in the literature have shown that the profile likelihood associated with the estimation of covariance range parameters for a Gaussian field can be multimodal. Here we complement these recent results by considering covariances with unknown nugget, scale and range parameters. Estimation is performed in a restricted maximum likelihood framework. For unbounded sampling domains and known range parameter, conditions ensuring asymptotic unimodality of the restr...
• INFERENCE FOR POISSON AND MULTINOMIAL MODELS FOR CAPTURE-RECAPTURE EXPERIMENTS AI翻译
  作者：CORMACK, RM; JUPP, PE
  摘要：Capture-recapture models have been formulated both as Poisson and as multinomial distributions. Maximum likelihood estimates of parameters under the two models are compared. For parameters which do not involve the population size the asymptotic covariances are shown to be the same.
• GLOBALLY CONVERGENT ALGORITHMS FOR MAXIMIZING A LIKELIHOOD FUNCTION AI翻译
  作者：JENSEN, ST; JOHANSEN, S; LAURITZEN, SL
  作者单位：Aalborg University
  摘要：In this paper we show global convergence, under very general assumptions, of iterative maximization procedures with cyclic fixing of groups of parameters, maximizing over the remaining parameters. Further we show that a slightly modified Newton procedure applied to the derivative of the reciprocal likelihood function in a one-dimensional exponential family, is globally convergent. We also prove that if the distribution of the sufficient statistic is infinitely divisible, then the Newton method...
• MULTIPLICATIVE EFFECTS IN MIXED MODEL ANALYSIS OF VARIANCE AI翻译
  作者：OMAN, SD
  摘要：A class of two-way mixed analysis of variance models is proposed, in which the fixed and random effects enter multiplicatively. Equations are developed for iterative computation of maximum likelihood estimates via a scoring algorithm. Parameter estimation and hypothesis testing are illustrated on a set of plant genetics data.