Survival Trees: An Alternative Non-Parametric Multivariate Technique for Life History Analysis

In this paper an extension of tree-structured methodology to cover censored survival analysis is discussed. Tree-based methods (also called recursive partitioning) provide a useful alternative to the classical survival data analysis techniques, such as the semi-parametric model of Cox, whenever the main purpose is defining groups of individuals, either with complete or censored life history, having different survival probability, based on the values of selected covariates. The essential feature of recursive partitioning is the construction of a decision rule in the form of a binary tree. Trees generally require fewer assumptions than classical methods and handle non standard and non linear data structures efficiently. Tree-growing methods make the processes of covariate selection and grouping of categories in event history models explicit. An example concerning the analysis of time to marriage of Italian women is presented.     

A new 'biased coin design' for the sequential allocation of two treatments

Summary.  Efron's biased coin design is a well-known randomization technique that helps to neutralize selection bias in sequential clinical trials for comparing treatments, while keeping the experiment fairly balanced. Extensions of the biased coin design have been proposed by several researchers who have focused mainly on the large sample properties of their designs. We modify Efron's procedure by introducing an adjustable biased coin design, which is more flexible than his. We compare it with other existing coin designs; in terms of balance and lack of predictability, its performance for small samples appears in many cases to be an improvement with respect to the other sequential randomized allocation procedures.     

Deciding with Thresholds: Importance Measures and Value of Information

Risk‐informed decision making is often accompanied by the specification of an acceptable level of risk. Such target level is compared against the value of a risk metric, usually computed through a probabilistic safety assessment model, to decide about the acceptability of a given design, the launch of a space mission, etc. Importance measures complement the decision process with information about the risk/safety significance of events. However, importance measures do not tell us whether the occurrence of an event can change the overarching decision. By linking value of information and importance measures for probabilistic risk assessment models, this work obtains a value‐of‐information‐based importance measure that brings together the risk metric, risk importance measures, and the risk threshold in one expression. The new importance measure does not impose additional computational burden because it can be calculated from our knowledge of the risk achievement and risk reduction worth, and complements the insights delivered by these importance measures. Several properties are discussed, including the joint decision worth of basic event groups. The application to the large loss of coolant accident sequence of the Advanced Test Reactor helps us in illustrating the risk analysis insights.     

Income Share Elasticity and Stochastic Dominance

Income share elasticity is a function π which can describe the size distribution of income (Esteban in Intern Econ Rev 27:439–444, 1986). On the other hand, the conventional density representation of the latter gives parameters of first or second order stochastic dominance (SD), widely used to describe shifts in income distribution, to which inequality measures are attached. The paper draws a link between the two, by providing conditions such that a given shift to π is equivalent to a first or second order SD shift of the distribution of income. Some applications to Lorenz rankings are also provided.     

Statistical Properties of Threshold Models

This article focuses the attention on the Self Exciting Threshold Autoregressive Moving Average model (SETARMA) proposed in Tong (1983 Tong , H. ( 1983 ). Threshold Models in Nonlinear Time Series Analysis . London : Springer-Verlag .[Crossref] , [Google Scholar]). The stochastic structure of the model is discussed and different specifications are presented. Starting from one of them, we give sufficient conditions for the weak stationarity of the model that are discussed and critically compared to other results given in literature. In particular, after showing that the SETARMA model belongs to the class of the Random Coefficients Autoregressive models, widely discussed in Nicholls and Quinn (1982 Nicholls , D. F. , Quinn , B. G. (1982). Random Coefficients Autoregressive Models. An Introduction . New York : Springer-Verlag.[Crossref] , [Google Scholar]), we give some issues on the weak stationarity of its stochastic structure that are more general than those given in the existing literature and appear not affected by the moving average component.     

The perception of the values of urban space by senior citizens of Valparaiso

This paper is framed within the context of the Fondecyt Research Project (No. 1061179) entitled “Senior Citizens' quality of life in an Urban Habitat: Valparaiso, a Case Study.” It investigates how certain topographical conditions in the urban habitat impact on the quality of life of the population's most senior age group. Following a theoretical approach, we discuss some of the initial results obtained in our field research, integrating a quantitative technique of territorial analysis, GIS maps, and the qualitative techniques of Focus Groups and ethnographic interviews. The study of senior citizens' perceptions of their quality of life in Valparaiso indicates that the city's irregular topography and character as a heritage city mediate how the elderly perceive the urban space, creating social networks and a sense of community uniquely their own.     

Low-pass filter design using locally weighted polynomial regression and discrete prolate spheroidal sequences

The paper concerns the design of nonparametric low-pass filters that have the property of reproducing a polynomial of a given degree. Two approaches are considered. The first is locally weighted polynomial regression (LWPR), which leads to linear filters depending on three parameters: the bandwidth, the order of the fitting polynomial, and the kernel. We find a remarkable linear (hyperbolic) relationship between the cut-off period (frequency) and the bandwidth, conditional on the choices of the order and the kernel, upon which we build the design of a low-pass filter.The second hinges on a generalization of the maximum concentration approach, leading to filters related to discrete prolate spheroidal sequences (DPSS). In particular, we propose a new class of low-pass filters that maximize the concentration over a specified frequency range, subject to polynomial reproducing constraints. The design of generalized DPSS filters depends on three parameters: the bandwidth, the polynomial order, and the concentration frequency. We discuss the properties of the corresponding filters in relation to the LWPR filters, and illustrate their use for the design of low-pass filters by investigating how the three parameters are related to the cut-off frequency.     

The Relationship Between Social Leisure and Life Satisfaction: Causality and Policy Implications

Social leisure is generally found to be positively correlated with life satisfaction in the empirical literature. We ask if this association captures a genuine causal effect by using panel data from the GSOEP. Our identification strategy exploits the change in social leisure brought about by retirement, since the latter is an event after which the time investable in (the outside job) relational life increases. We instrument social leisure with various measures of the age cohort specific probability of retirement. With such approach we document that social leisure has a positive and significant effect on life satisfaction. Our findings shed some light on the age-happiness pattern. Policy implications are also discussed.     

Ergonomic work analysis as a tool of prevention for the occupational safety and health management system

This paper introduces the Ergonomic Work Analysis as a relevant instrument to identify the risks in occupational environments through the investigation of factors that influence the relationship between the worker and the productive process. It draws a parallel between the several aspects of risk identification in traditional tools of Health and Safety Management and the factors embraced by the Ergonomic Work Analysis, showing that the ergonomic methodology is able to go deeper in the scenarios of possible incident causes. This deepening enables the establishment of a relationship between the work context and the upcoming damage to the physical integrity of the worker. It acts as a complementary instrument in the traditional approach to the risk management. In order to explain the application of this methodology in a preventive way, it is presented a case study of a coal mill inspector in a siderurgic company.