A latent variable model for analyzing mixed longitudinal (k,l)-inflated count and ordinal responses

A random effects model for analyzing mixed longitudinal count and ordinal data is presented where the count response is inflated in two points (k and l) and an (k,l)-Inflated Power series distribution is used as its distribution. A full likelihood-based approach is used to obtain maximum likelihood estimates of parameters of the model. For data with non-ignorable missing values models with probit model for missing mechanism are used.The dependence between longitudinal sequences of responses and inflation parameters are investigated using a random effects approach. Also, to investigate the correlation between mixed ordinal and count responses of each individuals at each time, a shared random effect is used. In order to assess the performance of the model, a simulation study is performed for a case that the count response has (k,l)-Inflated Binomial distribution. Performance comparisons of count-ordinal random effect model, Zero-Inflated ordinal random effects model and (k,l)-Inflated ordinal random effects model are also given. The model is applied to a real social data set from the first two waves of the national longitudinal study of adolescent to adult health (Add Health study). In this data set, the joint responses are the number of days in a month that each individual smoked as the count response and the general health condition of each individual as the ordinal response. For the count response there is incidence of excess values of 0 and 30.     

Reflective Supervision for Child Protection Practice – Reaching Beneath the Surface

This paper describes a model of reflective supervision for child protection practice that is informed by psychoanalytic theory. Eileen Munro promotes the idea of depth in practice, referring to the skills and knowledge required to undertake the work. Her reports highlight the importance of supervision to provide a thinking space for practitioners where reflection can take place. This paper will explore the importance of supervision in more depth and describe how psychoanalytic ideas can help us understand the complexities of our work with families, anchoring us to a solid theoretical base and helping to make sense of the maelstrom of feelings that are stirred in us as well as in our colleagues and clients.     

A second order model for cylindrical data

Modeling cylindrical data, comprised of a linear component and a directional component, can be done using Fourier series expansions if we consider the conditional distribution of the linear component given the angular component. This paper presents the second order model which is a natural extension of the Mardia and Sutton (1978) first order model. This model can be parameterized either in polar or Cartesian coordinates, and allows for parameter estimation using standard multiple linear regression. Characteristic of the new model, how to compare the adequacy of the fit for first and second order models, and an example involving wind direction and temperature are presented.     

Choosing Prior Hyperparameters: With Applications to Time-Varying Parameter Models

Time-varying parameter models with stochastic volatility are widely used to study macroeconomic and financial data. These models are almost exclusively estimated using Bayesian methods. A common practice is to focus on prior distributions that themselves depend on relatively few hyperparameters such as the scaling factor for the prior covariance matrix of the residuals governing time variation in the parameters. The choice of these hyperparameters is crucial because their influence is sizeable for standard sample sizes. In this article, we treat the hyperparameters as part of a hierarchical model and propose a fast, tractable, easy-to-implement, and fully Bayesian approach to estimate those hyperparameters jointly with all other parameters in the model. We show via Monte Carlo simulations that, in this class of models, our approach can drastically improve on using fixed hyperparameters previously proposed in the literature. Supplementary materials for this article are available online.     

Time‐Space Distanciation: An Interdisciplinary Account of How Culture Shapes the Implicit and Explicit Psychology of Time and Space

The growing body of research on temporal and spatial experience lacks a comprehensive theoretical approach. Drawing on Giddens’ framework, we present time‐space distanciation (TSD) as a construct for theorizing the relations between culture, time, and space. TSD in a culture may be understood as the extent to which (1) time and space are abstracted as separate dimensions and (2) activities are extended and organized across time and space. After providing a historical account of its development, we outline a multi‐level conceptualization of TSD supported by research on cultural differences in the experience of time and space. We impact this conceptualization by examining two ethnographic case studies. We conclude by highlighting future research directions. TSD is an integrative, interdisciplinary, multi level construct with the potential to guide the burgeoning social science of time and space.     

系列性权利组合:有限责任公司股东优先购买权性质新探

股东优先购买权体现出有限责任公司人合性与资合性之间的冲突。中国对于股东优先购买权的法律规范存在缺陷,其根本原因在于缺乏一套体系性的股东优先购买权理论。股东优先购买权本质上是系列性权利组合,而非单一的权利,具体包括资格维持请求权、告知请求权、强制缔约权和优先受领权。股东于其取得股东身份时取得优先购买权,并于此时得以行使。至于优先购买权的行使期限,则不宜做过多理论上的探讨,而应当交由立法者根据本国国情加以规定。     

农民社会阶层越高越幸福吗?——基于CGSS 2010-2015年数据的微观分析

针对农民社会阶层与幸福感问题，通过分析2010-2015年“中国综合社会调查”数据，采用有序响应Logit模型估计，实证检验了农民社会阶层定位的幸福效应。研究发现，农民社会阶层定位存在幸福效应，社会阶层定位越高的农民，其幸福感越强；进一步区域比较发现，此幸福效应在经济发达地区最为显著；而户籍比较发现，农民社会阶层定位的幸福效应低于城镇居民；但从发展趋势来看，农民社会阶层定位的幸福效应较10年前有所增强，且农民对社会阶层定位有较为乐观的预期。据此提出，民生建设中重视农民的发展诉求和精神需求，融通农民社会阶层上升的渠道，从而提高农民幸福感，保障农民公平共享社会发展的成果。     

A discrete-time risk model with Poisson ARCH claim-number process

Abstract

In this paper, we propose a discrete-time risk model with the claim number following an integer-valued autoregressive conditional heteroscedasticity (ARCH) process with Poisson deviates. In this model, the current claim number depends on the previous observations. Within this framework, the equation for finding the adjustment coefficient is derived. Numerical studies are also carried out to examine the impact of the Poisson ARCH dependence structure on the ruin probability.