论英语中“可能性”意义的外显与内隐推测

分别使用情态动词、副词、形容词、名词等方式,推测了行为发生或状态存在的外显形式;同时还运用不定代词、动词、从属连词、-ing形式等方式间接而含蓄地表示和推测了行为或状态存在的可能性。     

Design priorities and disciplinary perspectives: the case of the US National Children's Study

Summary.  The National Children's Study (NCS), which was undertaken in 2000 by collaboration between several US federal government agencies, is one of the largest and boldest longitudinal studies of children's health ever undertaken. One of the key design issues has been the nature of the NCS sample. The paper describes the nature of the choices and the reasons for the decision that the NCS be based on a national probability sample. Designed as a study of the environmental influences on children's health and development, the NCS is expected to identify, enrol and follow about 100000 children from their birth to the age of 21 years. A broad definition of relevant environments of interest, and a full partnership between government, university and medical scientists, introduces considerable challenges in the design of the study.     

Bayesian Inference in Dynamic Disequilibrium Models: An Application to the Polish Credit Market

We propose a Bayesian approach for inference in a dynamic disequilibrium model. To circumvent the difficulties raised by the Maddala and Nelson (1974) specification in the dynamic case, we analyze a dynamic extended version of the disequilibrium model of Ginsburgh et al. (1980). We develop a Gibbs sampler based on the simulation of the missing observations. The feasibility of the approach is illustrated by an empirical analysis of the Polish credit market, for which we conduct a specification search using the posterior deviance criterion of Spiegelhalter et al. (2002).     

In-Sample or Out-of-Sample Tests of Predictability: Which One Should We Use?

It is widely known that significant in-sample evidence of predictability does not guarantee significant out-of-sample predictability. This is often interpreted as an indication that in-sample evidence is likely to be spurious and should be discounted. In this paper, we question this interpretation. Our analysis shows that neither data mining nor dynamic misspecification of the model under the null nor unmodelled structural change under the null are plausible explanations of the observed tendency of in-sample tests to reject the no-predictability null more often than out-of-sample tests. We provide an alternative explanation based on the higher power of in-sample tests of predictability in many situations. We conclude that results of in-sample tests of predictability will typically be more credible than results of out-of-sample tests.     

Estimating the association parameter for copula models under dependent censoring

Summary. Many biomedical studies involve the analysis of multiple events. The dependence between the times to these end points is often of scientific interest. We investigate a situation when one end point is subject to censoring by the other. The model assumptions of Day and co-workers and Fine and co-workers are extended to more general structures where the level of association may vary with time. Two types of estimating function are proposed. Asymptotic properties of the proposed estimators are derived. Their finite sample performance is studied via simulations. The inference procedures are applied to two real data sets for illustration.     

Reference priors for exponential families

Reference analysis, introduced by Bernardo (J. Roy. Statist. Soc. 41 (1979) 113) and further developed by Berger and Bernardo (On the development of reference priors (with discussion). In: J.M. Bernardo, J.O. Berger, A.P. Dawid, A.F.M. Smith (Eds.), Bayesian Statistics, Vol. 4, Clarendon Press, Oxford, pp. 35–60), has proved to be one of the most successful general methods to derive noninformative prior distributions. In practice, however, reference priors are typically difficult to obtain. In this paper we show how to find reference priors for a wide class of exponential family likelihoods.     

Non parametric mixture priors based on an exponential random scheme

We propose a general procedure for constructing nonparametric priors for Bayesian inference. Under very general assumptions, the proposed prior selects absolutely continuous distribution functions, hence it can be useful with continuous data. We use the notion ofFeller-type approximation, with a random scheme based on the natural exponential family, in order to construct a large class of distribution functions. We show how one can assign a probability to such a class and discuss the main properties of the proposed prior, namedFeller prior. Feller priors are related to mixture models with unknown number of components or, more generally, to mixtures with unknown weight distribution. Two illustrations relative to the estimation of a density and of a mixing distribution are carried out with respect to well known data-set in order to evaluate the performance of our procedure. Computations are performed using a modified version of an MCMC algorithm which is briefly described.     

Bayesian variable selection and regularization for time–frequency surface estimation

Summary.  We describe novel Bayesian models for time–frequency inverse modelling of non-stationary signals. These models are based on the idea of a Gabor regression , in which a time series is represented as a superposition of translated, modulated versions of a window function exhibiting good time–frequency concentration. As a necessary consequence, the resultant set of potential predictors is in general overcomplete—constituting a frame rather than a basis—and hence the resultant models require careful regularization through appropriate choices of variable selection schemes and prior distributions. We introduce prior specifications that are tailored to representative time series, and we develop effective Markov chain Monte Carlo methods for inference. To highlight the potential applications of such methods, we provide examples using two of the most distinctive time–frequency surfaces—speech and music signals—as well as standard test functions from the wavelet regression literature.     

An iterative Monte Carlo method for nonconjugate Bayesian analysis

The Gibbs sampler has been proposed as a general method for Bayesian calculation in Gelfand and Smith (1990). However, the predominance of experience to date resides in applications assuming conjugacy where implementation is reasonably straightforward. This paper describes a tailored approximate rejection method approach for implementation of the Gibbs sampler when nonconjugate structure is present. Several challenging applications are presented for illustration.     

黑胸散白蚁肠道共生甲烷菌的初步研究

通过对黑胸散白蚁(Reticulitermes chinensis Snyder)肠道的显微观察发现在后肠肠壁上有很多甲烷菌。共生甲烷菌形态多为短杆状和长杆状,其中短杆状居多。同时,用非培养法,对黑胸散白蚁肠道共生甲烷菌的16SrR NA基因进行扩增并克隆,对代表不同RFLP谱型的五个克隆测序,系统发育分析表明,这些克隆均属于甲烷杆菌目。