Motivated by a breast cancer research program, this paper is concerned with the joint survivor function of multiple event times when their observations are subject to informative censoring caused by a terminating event. We formulate the correlation of the multiple event times together with the time to the terminating event by an Archimedean copula to account for the informative censoring. Adapting the widely used two-stage procedure under a copula model, we propose an easy-to-implement pseudo-likelihood based procedure for estimating the model parameters. The approach yields a new estimator for the marginal distribution of a single event time with semicompeting-risks data. We conduct both asymptotics and simulation studies to examine the proposed approach in consistency, efficiency, and robustness. Data from the breast cancer program are employed to illustrate this research.
A finite mixture model using the multivariate t distribution has been shown as a robust extension of normal mixtures. In this paper, we present a Bayesian approach for inference about parameters of t-mixture models. The specifications of prior distributions are weakly informative to avoid causing nonintegrable posterior distributions. We present two efficient EM-type algorithms for computing the joint posterior mode with the observed data and an incomplete future vector as the sample. Markov chain Monte Carlo sampling schemes are also developed to obtain the target posterior distribution of parameters. The advantages of Bayesian approach over the maximum likelihood method are demonstrated via a set of real data. 相似文献
This study reports on New Zealand dairy farmers’ access to and use of information as mediated through conditions of risk and trust within the context of their interpersonal social networks. We located participants’ reports of their information use within their perceived environments of trust and risk, following Giddens's [1990. The consequences of modernity. Polity Press, Stanford, CA] typology of trust and risk in pre-modernity and modernity. The research participants were constant users of interpersonal and print information from numerous sources, and monitored their incoming data in the light of strategic needs, reflecting their roles as both farming practitioners and business owners. Socio-spatial knowledge networks (SSKNs) combine individuals’ explanatory cognitive models of information acquisition and use with a micro-geographical analysis of their interpersonal networks. The participants showed characteristics of pre-modern, modern and even post-modern society in respect of their use of complex interactional forms, as well as a blending of individualistic and communitarian practices and concerns in their professional and personal lives. 相似文献
The aim of this paper is to measure the effects on household composition of changes in demographic events, e.g. mortality, fertility, marriage, divorce. British household data are taken from the General Household Survey and aged by simulation to 2001 using a ‘Most Likely’ model. Subsequently different assumptions of each demographic event are taken from 1991 so that the effects of perturbations within each event can be studied. Special features of the simulation model are the differentiations between cohabitation and marriage and separation and divorce, and the detailed breakdowns of household types such as lone parents into single and previously married women and men with children aged 0–4, 5–15 and 16 and over. 相似文献