1997

最新文章

• Bounded influence estimation in the mixed linear model AI翻译
  作者：Richardson, AM
  摘要：Bounded influence estimation (also known as generalized M or GM estimation) in the regression model is reviewed. The definitions of bounded influence estimation proposed by Mallows and Schweppe are then extended to the mixed linear model. This is achieved by applying appropriate weight functions to maximum likelihood and restricted maximum likelihood estimating equations. The asymptotic properties of the new estimators are obtained, and the estimators are applied to an artificial dataset. The ...
• Computing Bayes factors by combining simulation and asymptotic approximations AI翻译
  作者：DiCiccio, TJ; Kass, RE; Raftery, A; Wasserman, L
  作者单位：Carnegie Mellon University; University of Washington; University of Washington Seattle
  摘要：The Bayes factor is a ratio of two posterior normalizing constants. which may be difficult to compute. We compare several methods of estimating Bayes factors when it is possible to simulate observations from the posterior distributions, via Markov chain Monte Carlo or other techniques. The methods that we study are all easily applied without consideration of special features of the problem, provided that each posterior distribution is well behaved in the sense of having a single dominant mode....
• Mean and covariance structure analysis: Theoretical and practical improvements AI翻译
  作者：Yuan, KH; Bentler, PM
  作者单位：University of California System; University of California Los Angeles
  摘要：The most widely used multivariate statistical models in the social and behavioral sciences involve linear structural relations among observed and latent variables. In practice, these variables are generally nonnormally distributed; hence classical multivariate analysis, based on multinomial error-free variables having no simultaneous interrelations, is not adequate to deal with such data. A promising alternative, based on asymptotically distribution-free (ADF) covariance structure analysis, ha...
• Inference of vector autoregressive models with cointegration and scalar components AI翻译
  作者：Ahn, SK
  作者单位：Pohang University of Science & Technology (POSTECH)
  摘要：For the partially nonstationary vector autoregressive model of Ahn and Reinsel, I further assume that the first differenced series has scalar components of lower order and study estimation of these models along with asymptotic properties of the estimators. It is shown that Gaussian reduced rank estimation can be easily carried out by simple modification of the Ahn and Reinsel's method. The asymptotic distribution for the estimator of the nonstationary parameter is a locally asymptotically mixe...
• Nonparametric estimation of a mixing density via the kernel method AI翻译
  作者：Goutis, C
  作者单位：Universidad Carlos III de Madrid
  摘要：This article presents a method for estimating the latent distribution of a mixture model. The method is motivated by the standard kernel density estimation, but instead of using an estimate based on the unobserved latent variables, it takes the expectation with respect to their distribution conditional on the data. The resulting estimator is continuous and hence appropriate when there is a strong belief in the continuity of the mixing distribution. An asymptotic justification is presented, and...
• Interpolation methods for adapting to sparse design in nonparametric regression AI翻译
  作者：Hall, P; Turlach, BA
  摘要：We suggest interpolation methods for overcoming the problem of sparse design in local linear smoothing. These methods are based on simple rules, determined by the kernel and bandwidth, for deciding when and where pseudo-design points should be added to augment the original design sequence. New ordinates for the added design points are computed by simple interpolation, then local linear smoothing is applied directly to the expanded dataset. The method is competitive with alternatives (e.g., tho...
• A marginal likelihood approach to estimation in frailty models AI翻译
  作者：Lam, KF; Kuk, AYC
  作者单位：University of New South Wales Sydney
  摘要：A marginal likelihood approach is proposed for estimating the parameters ill a frailty model using clustered survival data. To overcome the analytic intractability of the marginal likelihood function, we propose a Monte Carlo approximation using the technique of importance sampling. Implementation is by means of simulations from the uniform distribution. The suggested method can cope with censoring and unequal cluster sizes and can be applied to any frailty distribution with explicit Laplace t...
• A voyage of discovery AI翻译
  作者：Billard, L
• Latent variable models for teratogenesis using multiple binary outcomes AI翻译
  作者：Legler, JM; Ryan, LM
  作者单位：Harvard University; Harvard T.H. Chan School of Public Health; Harvard University; Harvard University Medical Affiliates; Dana-Farber Cancer Institute
  摘要：Multiple outcomes are commonly measured in:the study of birth defects. The reason is that most teratogens do not cause a single, uniquely defined defect, but rather result in a range of effects, including major malformations, minor anomalies, and deficiencies in birth weight, length and head circumference. The spectrum of effects associated with a particular teratogen is sometimes described as a ''syndrome.'' In this article we develop a latent variable model to characterize exposure effects o...
• Optimum partition procedures for separating good and bad treatments AI翻译
  作者：Giani, G; Strassburger, K
  摘要：This article considers partitioning a given set of competing treatments into two subsets with the purpose of separating good and bad treatments. (The:qualities ''good'' and ''bad'' are defined in terms of the parameter vector of the underlying distribution family.) Regarding the probability of an incorrect partition, within the class of so-called natural procedures minimax solutions are derived under suitable distributional assumptions. These results extend previous investigations to the large...