Optimal sampling for repeated binary measurements

The authors consider the optimal design of sampling schedules for binary sequence data. They propose an approach which allows a variety of goals to be reflected in the utility function by including deterministic sampling cost, a term related to prediction, and if relevant, a term related to learning about a treatment effect To this end, they use a nonparametric probability model relying on a minimal number of assumptions. They show how their assumption of partial exchangeability for the binary sequence of data allows the sampling distribution to be written as a mixture of homogeneous Markov chains of order k. The implementation follows the approach of Quintana & Müller (2004), which uses a Dirichlet process prior for the mixture.     

Control centers design for ergonomics and safety

This paper shows the general design conditions about ergonomics and safety for control centers in the petrochemical process industry. Some of the topics include guidelines for the optimized workstation design, control room layout, building layout, and lighting, acoustical and environmental design. Also takes into account the safety parameters in the control rooms and centers design. The conditions and parameters shown in this paper come from the standards and global advances on this topic on the most recent publications. And also the work was supplemented by field visits of our team to the control center operations in a petrochemical company, and technical literature search efforts. This guideline will be useful to increase the productivity and improve the working conditions at the control rooms.     

Describing poverty in an ultraperipheral region: The case of the Canary Islands

Based on data from the Survey on Social Conditions in the Canary Islands of the Instituto Canario de Estadística, this study analyzes poverty in Canary households by island of residence, age, employment and the educational level of the main provider. In addition to accepted objective measures, we also estimate poverty using subjective methods, in which the poverty line is defined or obtained by the opinion of household members of their own level of poverty.     

Semiparametric Bayesian classification with longitudinal markers

Summary.  We analyse data from a study involving 173 pregnant women. The data are observed values of the β human chorionic gonadotropin hormone measured during the first 80 days of gestational age, including from one up to six longitudinal responses for each woman. The main objective in this study is to predict normal versus abnormal pregnancy outcomes from data that are available at the early stages of pregnancy. We achieve the desired classification with a semiparametric hierarchical model. Specifically, we consider a Dirichlet process mixture prior for the distribution of the random effects in each group. The unknown random-effects distributions are allowed to vary across groups but are made dependent by using a design vector to select different features of a single underlying random probability measure. The resulting model is an extension of the dependent Dirichlet process model, with an additional probability model for group classification. The model is shown to perform better than an alternative model which is based on independent Dirichlet processes for the groups. Relevant posterior distributions are summarized by using Markov chain Monte Carlo methods.     

On the recoding of continuous and bounded indexes to a binomial form: an application to quality-of-life scores

The statistical analysis of patient-reported outcomes (PROs) as endpoints has shown to be of great practical relevance. The resulting scores or indexes from the questionnaires used to measure PROs could be treated as continuous or ordinal. The goal of this study is to propose and evaluate a recoding process of the scores, so that they can be treated as binomial outcomes and, therefore, analyzed using logistic regression with random effects. The general methodology of recoding is based on the observable values of the scores. In order to obtain an optimal recoding, the evaluation of the recoding method is tested for different values of the parameters of the binomial distribution and different probability distributions of the random effects. We illustrate, evaluate and validate the proposed method of recoding with the Short Form-36 (SF-36) Survey and real data. The optimal recoding approach is very useful and flexible. Moreover, it has a natural interpretation, not only for ordinal scores, but also for questionnaires with many dimensions and different profiles, where a common method of analysis is desired, such as the SF-36.     

Cluster‐Specific Variable Selection for Product Partition Models

We propose a random partition model that implements prediction with many candidate covariates and interactions. The model is based on a modified product partition model that includes a regression on covariates by favouring homogeneous clusters in terms of these covariates. Additionally, the model allows for a cluster‐specific choice of the covariates that are included in this evaluation of homogeneity. The variable selection is implemented by introducing a set of cluster‐specific latent indicators that include or exclude covariates. The proposed model is motivated by an application to predicting mortality in an intensive care unit in Lisboa, Portugal.     

A semiparametric Bayesian model for repeatedly repeated binary outcomes

Summary.  We discuss the analysis of data from single-nucleotide polymorphism arrays comparing tumour and normal tissues. The data consist of sequences of indicators for loss of heterozygosity (LOH) and involve three nested levels of repetition: chromosomes for a given patient, regions within chromosomes and single-nucleotide polymorphisms nested within regions. We propose to analyse these data by using a semiparametric model for multilevel repeated binary data. At the top level of the hierarchy we assume a sampling model for the observed binary LOH sequences that arises from a partial exchangeability argument. This implies a mixture of Markov chains model. The mixture is defined with respect to the Markov transition probabilities. We assume a non-parametric prior for the random-mixing measure. The resulting model takes the form of a semiparametric random-effects model with the matrix of transition probabilities being the random effects. The model includes appropriate dependence assumptions for the two remaining levels of the hierarchy, i.e. for regions within chromosomes and for chromosomes within patient. We use the model to identify regions of increased LOH in a data set coming from a study of treatment-related leukaemia in children with an initial cancer diagnostic. The model successfully identifies the desired regions and performs well compared with other available alternatives.     

Pivotal Quantities Based on Sequential Data: A Bootstrap Approach

A bootstrap algorithm is provided for obtaining a confidence interval for the mean of a probability distribution when sequential data are considered. For this kind of data the empirical distribution can be biased but its bias is bounded by the coefficient of variation of the stopping rule associated with the sequential procedure. When using this distribution for resampling the validity of the bootstrap approach is established by means of a series expansion of the corresponding pivotal quantity. A simulation study is carried out using Wang and Tsiatis type tests and considering the normal and exponential distributions to generate the data. This study confirms that for moderate coefficients of variation of the stopping rule, the bootstrap method allows adequate confidence intervals for the parameters to be obtained, whichever is the distribution of data.     

Poverty dynamics and disability: An empirical exercise using the European community household panel

In this article, we analyze the influence of being disabled on poverty dynamics. We present results adjusting the modified OECD equivalence scale by the number of adults with severe disabilities in the household and calculate poverty rates with this disability-adjusted scale. In addition we address the problem of incidental parameters in econometric models for the probability of being poor and separate the effects of being disabled on poverty that are short and long-term. The results show that the long-term (‘persistence’) effect is three times the short-term (‘true state dependence’) effect, which has significant social policy implications.     

Analysis of the lifted weight including height and frequency factors for workers in Colombia

Factors related to the height of the load and the frequency of handling have become a way to predict the acceptable standard weight lifted for workers whose main task is the manual lifting of materials and measuring the conditions is important to determine a maximum weight lifted. This study was conducted to twenty (20) workers between eighteen (18) and forty (40) years old with a minimum six months experience and belonging to the warehouse and packaging area of a dairy products company. Consideration was given to three different heights such as knuckle, shoulder and total height as well as frequencies of 2, 4 and 6 times per minute. Average values for lifted weight were 17.9306 ± 2.37 kg. The conclusions and recommendations included a review of legislation related to Colombian maximum acceptable weight of lifting due to the current law does not match the acceptable weight handled in this research.