Parametric and Parametrically Smoothed Distribution-Free Proportional Hazard Models with Discrete Data

This paper discusses discrete time proportional hazard models and suggests a new class of flexible hazard functions. Explicitly modeling the discreteness of data is important since standard continuous models are biased; allowing for flexibility in the hazard estimation is desirable since strong parametric restrictions are likely to be similarly misleading. Simulation compare continuous and discrete models when data are generated by grouping and demonstrate that simple approximations recover underlying hazards well and outperform nonparametric maximum likelihood estimates in term of mean squared error.     

动态适应性生态经济区划模型及其应用

提高城市生态经济区划的精确性和动态性,对科学指导城市化发展具有重要的理论意义和应用价值。利用夜间灯光数据与人口密度建立线性模型,探索了以往用行政区域为最小统计单元数据的模拟细化问题;然后通过引入可变参数构建了动态适应性生态经济区划模型,在增强模型动态适应性的同时,将一级区划结果统一划分为生态管控区域、生态优先区域、优化开发区域和重点开发区域4个区域。以广州市增城区为典型案例,通过改进的动态适应性生态经济区划模型,运用GIS将增城区在两种情景下进行了模拟和对比,并提出了政策建议。区划结果符合当地发展特征,也为其他城市与区域的生态经济区划研究提供了科学方法。     

Epilogue — Demography and anthropology

As a conclusion, this paper reviews briefly the content of the volume. The wealth of demographic data has not been adequately exploited in anthropology; this is why this publication is valuable in showing attempts to apply demographic data in a variety of anthropological problems. This symposium has explored many interesting points which we recall here. Yet it has also opened up a whole range of further questions on the material presented as well as in this broad field. Several directions of research could be developed, for instance, testing among human populations, over long periods, the ecological thoughts of ecosystems evolving as a cascade of instabilities, rather than a succession of equilibrium states. Let us also recall the pervasive nature of demographic facts in topics such as the energy cycle or the genetic structure and evolution of human populations.     

The Mizon–Richard Encompassing Test for the Cox and Aalen Additive Hazards Models

Summary .   The Cox hazards model ( Cox, 1972 , Journal of the Royal Statistical Society, Series B 34, 187–220) for survival data is routinely used in many applied fields, sometimes, however, with too little emphasis on the fit of the model. A useful alternative to the Cox model is the Aalen additive hazards model ( Aalen, 1980 , in Lecture Notes in Statistics-2 , 1–25) that can easily accommodate time changing covariate effects. It is of interest to decide which of the two models that are most appropriate to apply in a given application. This is a nontrivial problem as these two classes of models are nonnested except only for special cases. In this article we explore the Mizon–Richard encompassing test for this particular problem. It turns out that it corresponds to fitting of the Aalen model to the martingale residuals obtained from the Cox regression analysis. We also consider a variant of this method, which relates to the proportional excess model ( Martinussen and Scheike, 2002 , Biometrika 89, 283–298). Large sample properties of the suggested methods under the two rival models are derived. The finite-sample properties of the proposed procedures are assessed through a simulation study. The methods are further applied to the well-known primary biliary cirrhosis data set.     

Fish population structuring in the North Sea: understanding processes and mechanisms from studies of the movements of adults

Few fish species form single, panmictic populations throughout their geographic range, most form subpopulations or 'stocks' with differing levels of interconnectivity. Different patterns of interconnectivity between subpopulations will give rise to different responses to exploitation and management, but they will also have different capacities to generate the genetic and phenotypic differences often used to discriminate between stocks. Consequently, knowledge of ontogenetic and seasonal patterns in the distribution, movement and behaviour of individuals is crucial to identifying population substructure. This paper considers the evidence gathered about movements and behaviour of adult fishes from mark-recapture and electronic tagging studies for a number of fish species in the North Sea and elsewhere in U.K. waters in an attempt to understand population structure and the processes that may give rise to it.     

Tissue-Characteristic Expression of Mouse Proteome

The mouse is a valuable model organism for biomedical research. Here, we established a comprehensive spectral library and the data-independent acquisition–based quantitative proteome maps for 41 mouse organs, including some rarely reported organs such as the cornea, retina, and nine paired organs. The mouse spectral library contained 178,304 peptides from 12,320 proteins, including 1678 proteins not reported in previous mouse spectral libraries. Our data suggested that organs from the nervous system and immune system expressed the most distinct proteome compared with other organs. We also found characteristic protein expression of immune-privileged organs, which may help understanding possible immune rejection after organ transplantation. Each tissue type expressed characteristic high-abundance proteins related to its physiological functions. We also uncovered some tissue-specific proteins which have not been reported previously. The testis expressed highest number of tissue-specific proteins. By comparison of nine paired organs including kidneys, testes, and adrenal glands, we found left organs exhibited higher levels of antioxidant enzymes. We also observed expression asymmetry for proteins related to the apoptotic process, tumor suppression, and organ functions between the left and right sides. This study provides a comprehensive spectral library and a quantitative proteome resource for mouse studies.     

An exploration of fixed and random effects selection for longitudinal binary outcomes in the presence of nonignorable dropout

We explore a Bayesian approach to selection of variables that represent fixed and random effects in modeling of longitudinal binary outcomes with missing data caused by dropouts. We show via analytic results for a simple example that nonignorable missing data lead to biased parameter estimates. This bias results in selection of wrong effects asymptotically, which we can confirm via simulations for more complex settings. By jointly modeling the longitudinal binary data with the dropout process that possibly leads to nonignorable missing data, we are able to correct the bias in estimation and selection. Mixture priors with a point mass at zero are used to facilitate variable selection. We illustrate the proposed approach using a clinical trial for acute ischemic stroke.