Estimability and Linear Hypotheses

A proposition is given which provides an easily justified reason as to why attention should be confined to estimable parametric vectors when formulating linear hypotheses. The possibility of justifying one's linear estimation effort on the estimable parametric functions via an identifiability condition is also mentioned.     

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.     

Dealing with label switching in mixture models

In a Bayesian analysis of finite mixture models, parameter estimation and clustering are sometimes less straightforward than might be expected. In particular, the common practice of estimating parameters by their posterior mean, and summarizing joint posterior distributions by marginal distributions, often leads to nonsensical answers. This is due to the so-called 'label switching' problem, which is caused by symmetry in the likelihood of the model parameters. A frequent response to this problem is to remove the symmetry by using artificial identifiability constraints. We demonstrate that this fails in general to solve the problem, and we describe an alternative class of approaches, relabelling algorithms , which arise from attempting to minimize the posterior expected loss under a class of loss functions. We describe in detail one particularly simple and general relabelling algorithm and illustrate its success in dealing with the label switching problem on two examples.     

Covariate selection for identifying the causal effects of stochastic interventions using causal networks

Suppose that cause-effect relationships between variables can be described by a causal network corresponding to a linear structural equation model. Kuroki and Miyakawa (2003) proposed a graphical criterion for selecting covariates to identify the causal effect of a conditional intervention. In this paper, we extend Kuroki and Miyakawa (2003) graphical criterion for selecting covariates to identify the causal effect of a stochastic intervention. Since stochastic intervention is a generalization of conditional intervention, our paper makes the results of Kuroki and Miyakawa (2003) more generally applicable.     

Bayesian Inference and Forecasting in Dynamic Neural Networks with Fully Markov Switching ARCH Noises

We deal with one-layer feed-forward neural network for the Bayesian analysis of nonlinear time series. Noises are modeled nonlinearly and nonnormally, by means of ARCH models whose parameters are all dependent on a hidden Markov chain. Parameter estimation is performed by sampling from the posterior distribution via Evolutionary Monte Carlo algorithm, in which two new crossover operators have been introduced. Unknown parameters of the model also include the missing values which can occur within the observed series, so, considering future values as missing, it is also possible to compute point and interval multi-step-ahead predictions.     

基于消费者需求与信息搜寻过程的新二维营销战略模型及验证

本项研究从消费者需求与消费者信息搜寻两个维度，将产品市场分为易识常用品市场、易识高档品市场、难识常用品市场、难识高档品市场。基于每一类产品的特征，我们提出不同产品市场的营销战略假设，然后在国内选择五个案例，对我们提出的营销战略假设进行了验证。     

A note on the prospective analysis of outcome-dependent samples

Summary. Two likelihood representations corresponding to the prospective and retrospective analyses of the case–control design are derived for general outcome-dependent samples with arbitrary discrete or continuous outcomes and possibly non-multiplicative models. Parameter identification in the general outcome-dependent design is reduced to the simple problem of parameter identification in the general odds ratio function. Both likelihoods are shown to generate the same profile likelihood for the common parameter of interest. Maximum like- lihood estimators based on either likelihood are semiparametric efficient for the identifiable parameters.