Emergence and Communication in Computational Sociology

Computational sociology models social phenomena using the concepts of emergence and downward causation. However, the theoretical status of these concepts is ambiguous; they suppose too much ontology and are invoked by two opposed sociological interpretations of social reality: the individualistic and the holistic. This paper aims to clarify those concepts and argue in favour of their heuristic value for social simulation. It does so by proposing a link between the concept of emergence and Luhmann's theory of communication. For Luhmann, society emerges from the bottom‐up as communication and he describes the process by which society limits the possible selections of individuals as downward causation. It is argued that this theory is well positioned to overcome some epistemological drawbacks in computational sociology.     

Estimation with right‐censored observations under a semi‐Markov model

The semi‐Markov process often provides a better framework than the classical Markov process for the analysis of events with multiple states. The purpose of this paper is twofold. First, we show that in the presence of right censoring, when the right end‐point of the support of the censoring time is strictly less than the right end‐point of the support of the semi‐Markov kernel, the transition probability of the semi‐Markov process is nonidentifiable, and the estimators proposed in the literature are inconsistent in general. We derive the set of all attainable values for the transition probability based on the censored data, and we propose a nonparametric inference procedure for the transition probability using this set. Second, the conventional approach to constructing confidence bands is not applicable for the semi‐Markov kernel and the sojourn time distribution. We propose new perturbation resampling methods to construct these confidence bands. Different weights and transformations are explored in the construction. We use simulation to examine our proposals and illustrate them with hospitalization data from a recent cancer survivor study. The Canadian Journal of Statistics 41: 237–256; 2013 © 2013 Statistical Society of Canada     

Block and Basu bivariate lifetime distribution in the presence of cure fraction

This paper presents estimates for the parameters included in the Block and Basu bivariate lifetime distributions in the presence of covariates and cure fraction, applied to analyze survival data when some individuals may never experience the event of interest and two lifetimes are associated with each unit. A Bayesian procedure is used to get point and confidence intervals for the unknown parameters. Posterior summaries of interest are obtained using standard Markov Chain Monte Carlo methods in rjags package for R software. An illustration of the proposed methodology is given for a Diabetic Retinopathy Study data set.     

刑事附带民事诉讼的赔偿范围

刑事附带民事诉讼是我国一项特殊且重要的诉讼制度，在司法实践中很有必要。然而，由于立法的不完善且随着人们对诉讼引发的深入研究和我国诉讼法实践的不断发展，此制度的弊端也不断暴露出来，引起了众多的争议。因此，有必要适时地对这一程序制度进行修改，使之具有科学性、合理性和可操作性，以充分体现诉讼效益原则和诉讼经济原则，实现刑事审判理论和实际的有效统一。     

Nonparametric regression estimation in a null recurrent time series

We derive an asymptotic theory of nonparametric estimation for a time series regression model Z_t=f(X_t)+W_t, where {X_t} and {Z_t} are observed nonstationary processes, and {W_t} is an unobserved stationary process. The class of nonstationary processes allowed for {X_t} is a subclass of the class of null recurrent Markov chains. This subclass contains the random walk, unit root processes and nonlinear processes. The process {W_t} is assumed to be linear and stationary.     

文献研究中语言学方法的限度

学术研究中,语言学方法常常用于文献的断代研究,但是,这一方法是具有一定限度的。若干失误往往表现在方法论的预设,凡是某一时代文献没有记载的语汇就不属于该时代;且在语汇的时间层次、语言使用的差异性、语法的时间层次上皆有一定的盲区。认识这一现象,有助于我们在使用上述方法进行学术研究时确定其存在的限度。     

Locally optimal designs for the simple death process

We demonstrate the existence and uniqueness of optimal observation schedules for discrete observation of the simple death process. Optimality is framed in the sense of observing the process at times that maximise the Fisher information for the death rate. We examine the relationship of our designs with those obtained by other authors for the simple birth process. A number of interesting properties are uncovered. Practical considerations in the application of these designs in an experimental setting are investigated, and we examine the performance of some approximately optimal designs that are usually simpler to implement. We will show that our optimal designs are highly robust to mis-specification of the death rate.     

Bayesian autoregressive spectral analysis

The article describes an operational Bayesian approach to making inferences for the spectral density function for univariate autoregressive processes and for the AR operator of multivariate autoregressive processes. The derivation of the approach is described. Numerical examples, including the Wolfer Sunspot numbers, are used to demonstrate the practical usefulness of the approach.     

Construction of a Markov Chain with Given Stationary Distribution

Given a rational, finite probability vector, a Markov chain is constructed having the given vector as its stationary distribution.