Analysis of some ruin-related quantities in a Markov-modulated risk model

In this paper, we study the joint Laplace transform and probability generating function of some random quantities that occur in each environment state by the time of ruin in a Markov-modulated risk process. These quantities include the duration spent in each state, the number of claims and the aggregate amount of claims that occurred in each state by the time of ruin. Explicit formulae for the joint transforms, given the initial surplus, and the initial and terminal environment states, are expressed in terms of a matrix version of the scale function. Moments and covariances of these ruin-related quantities are obtained and numerical illustrations are presented. The joint transform of the duration spent in each state, the number of claims, and the aggregate amount of claims that occurred in each state by the time the surplus attains a certain level are also investigated.     

Robust Smoothed Rank Estimation Methods for Accelerated Failure Time Model Allowing Clusters

Smoothed Gehan rank estimation methods are widely used in accelerated failure time (AFT) models with/without clusters. However, most methods are sensitive to outliers in the covariates. In order to solve this problem, we propose robust approaches based on the smoothed Gehan rank estimation methods for the AFT model, allowing for clusters by employing two different weight functions. Simulation studies show that the proposed methods outperform existing smoothed rank estimation methods regarding their biases and standard deviations when there are outliers in the covariates. The proposed methods are also applied to a real dataset from the “Major cardiovascular interventions” study.     

Collective versus Individual Effects in Survival Analysis of Multiple Failures

In survival analysis, we sometimes encounter data with multiple censored outcomes. Under certain scenarios, partial or even all covariates have ‘similar’ relative risks on the multiple outcomes in the Cox regression analysis. The similarity in covariate effects can be quantified using the proportionality of regression coefficients. Identifying the proportionality structure, or equivalently whether covariates have individual or collective effects, may have important scientific implications. In addition, it can lead to a smaller set of unknown parameters, which in turn results in more accurate estimation. In this article, we develop a novel approach for identifying the proportionality structure. Simulation shows the satisfactory performance of the proposed approach and its advantage over estimation under no assumed structure. We analyse three datasets to demonstrate the practical application of the proposed approach.     

Indirect membership function assignment based on ordinal regression

In many fuzzy sets applications, fuzzy membership functions are commonly developed based on empirical or expert knowledge. The equation of a membership function is usually determined somewhat arbitrarily. This paper explores a novel membership function design method based on ordinal regression analysis. The estimated thresholds between ordinal measurement categories are applied to calculate the intersection points between fuzzy sets. These intersection points are further applied to determine the equations of the membership functions. Information distortion due to empirical guess can thus be reduced and more latent information in the fuzzy responses can therefore be captured. A case study investigating the relationship between foster mothers’ satisfaction and the foster time and information provided has been conducted in this research. The applicability and effectiveness of the proposed membership function assignment approach have been demonstrated through several case studies.     

Stochastic comparisons on sample extremes of dependent and heterogenous observations

Stochastic comparison on order statistics from heterogeneous-dependent observations has been paid lots of attention recently. This paper devotes to investigating the ordering properties of order statistics from dependent observations. We derive the usual stochastic order for sample minimums and the second smallest order statistic, the dispersive order and the star order for minimums of samples having proportional hazards and Archimedean survival copulas. Similar ordering results are also obtained for maximums and the second largest order statistic of samples having proportional reversed hazards and Archimedean copulas. Several examples illustrating the main results are presented as well.     

城市滨水旧住区问题分析及对策研究——以上海市长风新村街道沿河旧住区为例

一、引言 进入21世纪以来,城市滨水地区由于其悠久的发展历史、丰厚的文化底蕴和重要的城市区位,成为政府、公众和开发商关注的焦点,因此,滨水地带的复兴成为中国城市发展的中心议题之一.     

城市河道生态景观规划理论与实践的探讨

自20世纪50年代,城市水域空间的规划与改造就成为国内外城市景观规划和生态建设的一个热点.与传统的以单一水域治理为目的的河道规划不同,现代城市水域空间规划强调以生态理论为依据,将水体、堤岸、湿地、植被等诸多要素统筹考虑,进行防洪蓄水、生态恢复、休闲娱乐、经济开发等综合规划.近几年,在国内多个城市,以河道改造为主的城市滨水区开发,成为城市景观设计和生态规划的焦点.     

Migration Decisions of Dual-earner Families: An Application of Multilevel Modeling

A comprehensive framework for guiding analyses of internal migration is lacking. This study contributes to the family migration literature in three important ways. We develop a multilevel theoretical framework emphasizing an integration of individual-, family-, and neighborhood-level effects; introduce multilevel statistical modeling; and explicitly assess how effects of economic-based explanatory variables vary by gender. Our data are from the Panel Study of Income Dynamics (PSID). We find that the likelihood of a family migrating is affected by economic and non-economic factors, some of which vary by gender. We add to the dual-earner migration literature by finding that wives are not likely to be tied-movers, but husbands are likely to be tied-stayers. Neighborhood factors also are important to the decision to migrate.