The use of simple reparameterizations to improve the efficiency of Markov chain Monte Carlo estimation for multilevel models with applications to discrete time survival models

Summary.  We consider the application of Markov chain Monte Carlo (MCMC) estimation methods to random-effects models and in particular the family of discrete time survival models. Survival models can be used in many situations in the medical and social sciences and we illustrate their use through two examples that differ in terms of both substantive area and data structure. A multilevel discrete time survival analysis involves expanding the data set so that the model can be cast as a standard multilevel binary response model. For such models it has been shown that MCMC methods have advantages in terms of reducing estimate bias. However, the data expansion results in very large data sets for which MCMC estimation is often slow and can produce chains that exhibit poor mixing. Any way of improving the mixing will result in both speeding up the methods and more confidence in the estimates that are produced. The MCMC methodological literature is full of alternative algorithms designed to improve mixing of chains and we describe three reparameterization techniques that are easy to implement in available software. We consider two examples of multilevel survival analysis: incidence of mastitis in dairy cattle and contraceptive use dynamics in Indonesia. For each application we show where the reparameterization techniques can be used and assess their performance.     

宁夏产业结构及其区位优势变化(1978～2003)

本文在已有的区位商分析方法基础上,提出了“产业综合区位商”概念,并以此作为衡量地区产业综合区位优势的尺度。借助于计量模型,分析了1978～2003年宁夏回族自治区六部门(农业、工业、建筑业、交通通讯业、商业、其他服务业)综合区位商变化趋势。结果如下:第一,宁夏产业综合区位商变化带有明显的阶段性特征。20世纪90年代初以来全面推行的市场化改革,提高了宁夏各产业部门的资源配置效率及其综合区位优势。第二,农村剩余劳动力向具有比较优势的非农产业转移,有利于优化地区产业结构。第三,各部门对宁夏产业综合区位优势变化有着不同的影响。建筑业影响力最大,其余依次是:其他服务业、农业、交通通讯业、商业、工业。工业的影响力最弱。如何加快工业化和工业部门优势产业的发展,是宁夏产业结构调整的关键。     

试论湘西民族地区交通经济带的评价与构建

本文从交通经济带的传统构成要素入手,对湘西民族地区交通基础设施、城镇综合实力、产业状况等进行了评价,提出了以民族文化为纽带构筑民族地区交通经济带的模式,认为该地区可以形成一纵一横两条沿路型经济子带,并提供具体设计思路。     

Seasonal patterns of global oil consumption: Implications for long term energy policy

The dynamic evolution of the seasonal patterns in world oil consumption is dictated by complex interactions between regional consumers. Although this global pattern was stable and predictable in the past, recently it has undergone dramatic changes that have not been well understood yet. This paper contributes to literature on oil consumption behaviours by analysing the counter-balance of ‘coincident’ and ‘counter-directional’ regional seasonal patterns that have time-varying amplitude relative to their longer-term trends. It is shown that the recent global seasonal changes have been mainly driven by long-run demand trends in fast-growing emerging markets and, to a lesser extent, by idiosyncratic changes in regions’ seasonal amplitude. Our analysis is relevant to energy policy in general as both global and regional oil consumption seasonality have important implications for oil pricing, investment decisions, hedging, geopolitics and energy security.     

Locative Mobile Social Networks: Mapping Communication and Location in Urban Spaces

Abstract

This study conceptualizes the new spatial logic created by the social use of location aware mobile technologies, analyzing how mobile communities are formed by the mapping of social networks in urban spaces. It explores two main areas with the goal of understanding how locative mobile social networks (LMSNs) challenge the traditional logic of networks. First, it conceptualizes LMSNs by comparing them to (1) traditional transportation and communication networks, and (2) mobile social networks (MSNs). Second, the paper discusses potential social implications of LMSNs, such as privacy, surveillance, and social exclusion.     

A generalization of the logistic linear'model

Consider the logistic linear model, with some explanatory variables overlooked. Those explanatory variables may be quantitative or qualitative. In either case, the resulting true response variable is not a binomial or a beta-binomial but a sum of binomials. Hence, standard computer packages for logistic regression can be inappropriate even if an overdispersion factor is incorporated. Therefore, a discrete exponential family assumption is considered to broaden the class of sampling models. Likelihood and Bayesian analyses are discussed. Bayesian computation techniques such as Laplacian approximations and Markov chain simulations are used to compute posterior densities and moments. Approximate conditional distributions are derived and are shown to be accurate. The Markov chain simulations are performed effectively to calculate posterior moments by using the approximate conditional distributions. The methodology is applied to Keeler's hardness of winter wheat data for checking binomial assumptions and to Matsumura's Accounting exams data for detailed likelihood and Bayesian analyses.     

On Variance Upper Bounds for Unimodal Distributions

We obtain upper bounds on the variance of discrete unimodal distributions. The alternative proofs of the corresponding bounds for the continuous unimodal distributions are also given.     

Heteroscedasticity-robust estimation of autocorrelation

This paper proposes estimators of the first-order autocorrelation that are based on suitably transformed ratios of successive observations. The new estimators are given by simple functions of the observations. Numerical optimization is not required. Simulations show that they are highly robust against extreme values and clusters of high volatility and are therefore particularly useful for the estimation of serial correlation in return series. Besides, the results of the simulation study also call into question the common practice of correcting the small-sample bias of conventional estimators.     

A Note on Identification in the Multinomial Probit Model

Although formal conditions for identification in the multinomial probit (MNP) model are now clearly established, little is known about how various estimable MNP specifications perform in practice. This article shows that parameter identification in the MNP model is extremely tenuous in the absence of exclusion restrictions. This previously unnoticed fact is important because formal identification of MNP models does not require exclusion restrictions, and many potential economic applications of MNP are to situations in which exclusion restrictions are not readily available. Thus, failure to be aware of the difficulties present in such situations may lead to reporting of unreliable results.     

Applying statistical tests to empirically compare tabu search parameters for MAX 3-SATISFIABILITY: A case study

The application of stochastic heuristic, like tabu search or simulated annealing, to address hard discrete optimization problems has been an important advance for efficiently obtaining good solutions in a reasonable amount of computing time. A challenge when applying such heuristics is assessing when a particular set of parameter values yields better performance compared to other such sets of parameter values. For example, it can be difficult to determine the optimal mix of memory types to incorporate into tabu search. This in turn prompts users to undertake a trial and error phase to determine the best combination of parameter settings for the problem under study. Moreover, for a given problem instance, one set of heuristic parameter settings may yield a better solution than another set of parameters, for a given initial solution. However, the performance of this heuristic on this instance for a single heuristic execution is not sufficient to assert that the first set of parameter settings will always produce superior results than the second set of parameters, for all initial solutions.