A Bayesian cluster analysis of election results

A Bayesian cluster analysis for the results of an election based on multinomial mixture models is proposed. The number of clusters is chosen based on the careful comparison of the results with predictive simulations from the models, and by checking whether models capture most of the spatial dependence in the results. By implementing the analysis on five recent elections in Barcelona, the reader is walked through the choice of the best statistics and graphical displays to help chose a model and present the results. Even though the models do not use any information about the location of the areas in which the results are broken into, in the example they uncover a four-cluster structure with a strong spatial dependence, that is very stable over time and relates to the demographic composition.     

Networks Matter: Male Mexican Migrants’ Use of Hospitals

Using the behavioral model for health care use and unique data from the Mexican migration project, we explore the factors associated with hospital use among Mexican male migrants to the US. Twenty-eight percent of the respondents reported receiving healthcare from a hospital. In addition to established enabling factors of time in the US and English language ability, this research points to the importance of family social networks as enabling hospital health care use among migrants. Migrants with more ties to family members with migration experience have an increased likelihood of using emergency room healthcare. The research adds to the body of literature by providing evidence that ties to individuals with US migration experience increases migrants’ probability of accessing healthcare at hospitals.     

巴塞罗那的设计和创新

设计在巴塞罗那的生活中具有重要作用.在过去的几百年当中,设计是促进巴塞罗那产生创造力的背后推动力. 大约一百年前,我们在巴塞罗那开展了总体设计,比较著名的设计师有高迪以及和他同期的设计师.     

A breakpoint detection in the mean model with heterogeneous variance on fixed time intervals

Statistics and Computing - This work is motivated by an application for the homogenization of global navigation satellite system (GNSS)-derived integrated water vapour series. Indeed, these series...     

Too Big to Learn: The Effects of Major Acquisition Failures on Subsequent Acquisition Divestment

We examine whether firms learn from their major acquisition failures. Drawing from a threat‐rigidity theoretical framework, we suggest that firms do not learn from their major acquisition failures. Furthermore, we hypothesize that host‐country experience reinforces the negative effects of major acquisition failures. Our research hypotheses are tested using an event history analysis of 741 acquisitions undertaken by French listed and non‐listed firms in the USA between January 1988 and December 2008. We use failure divestment (divestment resulting from acquisition failure) as a proxy for acquisition performance. Consistent with our theoretical framework, we find that major acquisition failures have a negative impact on future acquisition performance. Furthermore, we find that such negative effects are reinforced by firms’ host‐country experience.     

Experimentation order with good properties for 2 k factorial designs

Randomizing the order of experimentation in a factorial design does not always achieve the desired effect of neutralizing the influence of unknown factors. In fact, with some very reasonable assumptions, an important proportion of random orders achieve the same degree of protection as that obtained by experimenting in the design matrix standard order. In addition, randomization can induce a large number of changes in factor levels and thus make experimentation expensive and difficult. De Leon et al. [Experimentation order in factorial designs with 8 or 16 runs, J. Appl. Stat. 32 (2005), pp. 297–313] proposed experimentation orders for designs with eight or 16 runs that combine an excellent level of protection against the influence of unknown factors, with the minimum number of changes in factor levels. This article presents a new methodology to obtain experimentation orders with the desired properties for designs with any number of runs.     

Space-time estimation of a particle system model

Let X be a discrete time contact process (CP) on ?², as defined by Durrett and Levin (1994, Stochastic spatial models: a user's guide to ecological applications. Philosophical Transactions of the Royal Society of London Series B, 343, 329–350). We study the estimation of the model based on space-time evolution of X, that is, T + 1 successive observations of X on a finite subset S of sites. We consider the maximum marginal pseudo-likelihood (MPL) estimator and show that, when T→∞, this estimator is consistent and asymptotically normal for a non-vanishing supercritical CP. Numerical studies confirm these theoretical ones.     

Non-parametric inference for the effect of a treatment on survival times with application in the health and social sciences

In this paper we perform inference on the effect of a treatment on survival times in studies where the treatment assignment is not randomized and the assignment time is not known in advance. Two such studies are discussed: a heart transplant program and a study of Swedish unemployed eligible for employment subsidy. We estimate survival functions on a treated and a control group which are made comparable through matching on observed covariates. The inference is performed by conditioning on waiting time to treatment, that is, time between the entrance in the study and treatment. This can be done only when sufficient data are available. In other cases, averaging over waiting times is a possibility, although the classical interpretation of the estimated survival functions is lost unless hazards are not functions of waiting time. To show unbiasedness and to obtain an estimator of the variance, we build on the potential outcome framework, which was introduced by J. Neyman in the context of randomized experiments, and adapted to observational studies by D.B. Rubin. Our approach does not make parametric or distributional assumptions. In particular, we do not assume proportionality of the hazards compared. Small sample performance of the estimator and a derived test of no treatment effect are studied in a Monte Carlo study.     

Comparison of normal probability plots and dot plots in judging the significance of effects in two level factorial designs

In this article, we present a study carried out to compare the effectiveness of the normal probability plot (NPP) and a simple dot plot in assessing the significance of the effects in experimental designs with factors at two levels (2 ^k?p designs). Several groups of students who had just completed a course that covered factorial designs were asked to identify the significant effects in a total of 32 situations, 16 of which were represented using NPPs and the other 16 using dot plots. Although the 32 scenarios were said to be different, there were really only 16 different situations, each of which was represented using the two methods to be compared. A simple graphical analysis shows no evidence that there is a difference between the two procedures. However, in designs with 16 runs there are some cases where NPP seems to give slightly better results.