The Latin American cohabitation boom, 1970–2007

The article describes the rise of unmarried cohabitation in Latin American countries during the last 30 years of the twentieth century, both at the national and regional levels. It documents that this major increase occurred in regions with and without traditional forms of cohabitation alike. In addition, the striking degree of catching up of cohabitation among the better-educated population segments is illustrated. The connections between these trends and economic (periods of high inflation) and cultural (reduction of stigmas in ethical domains) factors are discussed. The conclusion is that the periods of inflation and hyperinflation may have been general catalysts, but no clear indications of correlation were found between such economic factors and the rise in cohabitation. The shift toward more tolerance for hitherto stigmatized forms of conduct (e.g., homosexuality, euthanasia, abortion, singleparent household) is in line with the rise of cohabitation in regions of Argentina, Chile, and Brazil where cohabitation used to be uncommon. Further rises in cohabitation during the first decade of the twenty-first century are expected in a number of countries (e.g., mexico) despite conditions of much lower inflation.     

Landslide Risk Models for Decision Making

This contribution presents a quantitative procedure for landslide risk analysis and zoning considering hazard, exposure (or value of elements at risk), and vulnerability. The method provides the means to obtain landslide risk models (expressing expected damage due to landslides on material elements and economic activities in monetary terms, according to different scenarios and periods) useful to identify areas where mitigation efforts will be most cost effective. It allows identifying priority areas for the implementation of actions to reduce vulnerability (elements) or hazard (processes). The procedure proposed can also be used as a preventive tool, through its application to strategic environmental impact analysis (SEIA) of land-use plans. The underlying hypothesis is that reliable predictions about hazard and risk can be made using models based on a detailed analysis of past landslide occurrences in connection with conditioning factors and data on past damage. The results show that the approach proposed and the hypothesis formulated are essentially correct, providing estimates of the order of magnitude of expected losses for a given time period. Uncertainties, strengths, and shortcomings of the procedure and results obtained are discussed and potential lines of research to improve the models are indicated. Finally, comments and suggestions are provided to generalize this type of analysis.     

Economic design of &Xmacron; charts with variable parameters: the Markov chain approach

This paper presents an economic design of &Xmacron; control charts with variable sample sizes, variable sampling intervals, and variable control limits. The sample size n, the sampling interval h, and the control limit coefficient k vary between minimum and maximum values, tightening or relaxing the control. The control is relaxed when an &Xmacron; value falls close to the target and is tightened when an &Xmacron; value falls far from the target. A cost model is constructed that involves the cost of false alarms, the cost of finding and eliminating the assignable cause, the cost associated with production in an out-of-control state, and the cost of sampling and testing. The assumption of an exponential distribution to describe the length of time the process remains in control allows the application of the Markov chain approach for developing the cost function. A comprehensive study is performed to examine the economic advantages of varying the &Xmacron; chart parameters.     

More on preference and freedom

The paper seeks to formalize the notion of effective freedom or the freedom to realize meaningful choices. The definition of meaningful choice used in this paper is based on the preference orderings that a reasonable person may have. I argue that only alternatives that can be selected by a reasonable person from the set of all possible alternatives provide a meaningful choice. I discuss this approach and provide an axiomatization of the cardinality rule and two lexicographic versions of this rule in this context. Received: 24 September 1996/Accepted: 26 January 2000     

An exploratory analysis of treatment completion and client and organizational factors using hierarchical linear modeling

Data from the Alcohol and Drug Services Study (ADSS) are used to analyze the structure and operation of the substance abuse treatment industry in the United States. Published literature contains little systematic empirical analysis of the interaction between organizational characteristics and treatment outcomes. This paper addresses that deficit. It develops and tests a hierarchical linear model (HLM) to address questions about the empirical relationship between treatment inputs (industry costs, types and use of counseling and medical personnel, diagnosis mix, patient demographics, and the nature and level of services used in substance abuse treatment), and patient outcomes (retention and treatment completion rates). The paper adds to the literature by demonstrating a direct and statistically significant link between treatment completion and the organizational and staffing structure of the treatment setting. Related reimbursement issues, questions for future analysis, and limitations of the ADSS for this analysis are discussed.     

Some Comments Regarding the Synthetic Chart

The steady-state average run length (ARL) is a function of the in-control probabilities of being in each nonabsorbing state. Davis and Woodall (2002) tabulated values that are significantly smaller than the steady-state ARLs, because they used the out-of-control probabilities. The synthetic chart signals when a second sample point falls beyond the control limits, no matter whether one of them falls above the centerline and the other falls below it. The side-sensitive version of the synthetic chart does not signal when the points beyond the control limits are on opposite sides. With this rule, the chart detects mean changes more quickly.     

Estimation,prediction and interpretation of NGG random effects models: an application to Kevlar fibre failure times

We propose a class of Bayesian semiparametric mixed-effects models; its distinctive feature is the randomness of the grouping of observations, which can be inferred from the data. The model can be viewed under a more natural perspective, as a Bayesian semiparametric regression model on the log-scale; hence, in the original scale, the error is a mixture of Weibull densities mixed on both parameters by a normalized generalized gamma random measure, encompassing the Dirichlet process. As an estimate of the posterior distribution of the clustering of the random-effects parameters, we consider the partition minimizing the posterior expectation of a suitable class of loss functions. As a merely illustrative application of our model we consider a Kevlar fibre lifetime dataset (with censoring). We implement an MCMC scheme, obtaining posterior credibility intervals for the predictive distributions and for the quantiles of the failure times under different stress levels. Compared to a previous parametric Bayesian analysis, we obtain narrower credibility intervals and a better fit to the data. We found that there are three main clusters among the random-effects parameters, in accordance with previous frequentist analysis.     

Coalition Formation and Stability

This paper studies a class of NTU coalition formation games in which every player’s payoff depends only on the members of her coalition. We identify four natural conditions on individuals’ preferences and show that, under each condition, stable (core) allocations exists.     

Nonparametric density estimation for censored survival data: Regression-spline approach

A method for nonparametric estimation of density based on a randomly censored sample is presented. The density is expressed as a linear combination of cubic M -splines, and the coefficients are determined by pseudo-maximum-likelihood estimation (likelihood is maximized conditionally on data-dependent knots). By using regression splines (small number of knots) it is possible to reduce the estimation problem to a space of low dimension while preserving flexibility, thus striking a compromise between parametric approaches and ordinary nonparametric approaches based on spline smoothing. The number of knots is determined by the minimum AIC. Examples of simulated and real data are presented. Asymptotic theory and the bootstrap indicate that the precision and the accuracy of the estimates are satisfactory.