RESUMO
En este artículo se analiza el perfil de ahorro de los hogares rurales y urbanos en México. A partir de la Encuesta Nacional de Ingresos y Gastos de los Hogares de 1994 a 2014 se construye un panel sintético y se estima un modelo semiparamétrico que permite identificar los perfiles por edades. Los resultados contrastan con la hipótesis del ciclo de vida, el perfil por edades no muestra una forma de U invertida, hay evidencia de mayor ahorro en las edades avanzadas. Los perfiles de ahorro son mayores en los hogares urbanos, en particular en aquellos con personas mayores y acceso a la salud.
This paper analyzes the saving profile of rural and urban Mexican households. Based on the National Survey of Household Income and Expenditure from 1994 to 2014, a synthetic panel is constructed and a semi-parametric model is estimated to identify profiles by age. Results show a contrast with the life cycle hypothesis. The age profile does not show an inverted U shape and there is evidence of greater savings in advanced ages. Saving profiles are higher in urban households, particularly for the elderly and regarding access to health.
Este artigo analisa o perfil de poupança de famílias rurais e urbanas no México. Com base na Pesquisa Nacional de Renda e Despesa Domiciliar de 1994 a 2014, é construído um painel sintético e estimado um modelo semiparamétrico que permite identificar os perfis por idade. Os resultados contrastam com a hipótese do ciclo de vida. O perfil da idade não apresenta a forma de U invertido e há evidências de maior economia em idades avançadas. Os perfis de poupança são mais elevados nos domicílios urbanos, especialmente naqueles com idosos e com acesso à saúde.
Assuntos
Humanos , Zona Rural , Saúde do Idoso , Estágios do Ciclo de Vida , México , Fatores Socioeconômicos , Estudos de Coortes , Acesso Universal aos Serviços de Saúde , RendaRESUMO
The degradation of solutions of the antibiotic levofloxacin (LVN) in sulfate medium at pH 3.0 has been investigated at pre-pilot scale by solar photoelectro-Fenton (SPEF) process. The flow plant included an FM01-LC filter-press cell equipped with a Ti|Pt anode and a three-dimensional-like air-diffusion cathode, connected to a compound parabolic collector as photoreactor and a continuous stirred tank under recirculation batch mode. The effect of volumetric flow rate on H2O2 electrogeneration from O2 reduction was assessed. Then, the influence of initial LVN concentration and Fe2+ concentration as catalyst on dissolved organic carbon (DOC) removal was thoroughly investigated. LVN was gradually mineralized by SPEF process, with faster DOC abatement at 0.50â¯mM Fe2+, yielding 100% after 360â¯minâ¯at applied cathodic potential of -0.30â¯V|SHE. The high mineralization current efficiency (MCE) and low specific energy consumption (ECDOC) revealed the extraordinary role of homogeneous hydroxyl radicals and natural UV light, which allowed the degradation of the antibiotic and its by-products with MCE values greater than 100%. Five cyclic by-products, N,N-diethylformamide and three short-chain linear carboxylic acids were detected by GC-MS and HPLC analyses. A parametric model to simulate the DOC decay versus electrolysis time was implemented for the SPEF pre-pilot flow plant, showing good agreement with experimental data.
Assuntos
Antibacterianos/análise , Peróxido de Hidrogênio/química , Ferro/química , Levofloxacino/análise , Modelos Teóricos , Raios Ultravioleta , Poluentes Químicos da Água/análise , Purificação da Água/métodos , Antibacterianos/efeitos da radiação , Catálise , Relação Dose-Resposta a Droga , Técnicas Eletroquímicas , Levofloxacino/efeitos da radiação , Projetos Piloto , Solubilidade , Fatores de Tempo , Poluentes Químicos da Água/efeitos da radiaçãoRESUMO
Sequentially randomized designs are commonly used in biomedical research, particularly in clinical trials, to assess and compare the effects of different treatment regimes. In such designs, eligible patients are first randomized to one of the initial therapies, then patients with some intermediate response (e.g. without progressive diseases) are randomized to one of the maintenance therapies. The goal is to evaluate dynamic treatment regimes consisting of an initial therapy, the intermediate response, and a maintenance therapy. In this article, we demonstrate the use of pattern-mixture model (commonly used for analyzing missing data) for estimating the effects of treatment regimes based on familiar survival analysis techniques such as Nelson-Aalen and parametric models. Moreover, we demonstrate how to use estimates from pattern-mixture models to test for the differences across treatment regimes in a weighted log-rank setting. We investigate the properties of the proposed estimators and test in a Monte Carlo simulation study. Finally we demonstrate the methods using the long-term survival data from the high risk neuroblastoma study.
RESUMO
The prediction of total egg production (TEP) potential in poultry is an important task to aid optimized management decisions in commercial enterprises. The objective of the present study was to compare different modeling approaches for prediction of TEP in meat type quails (Coturnix coturnix coturnix) using phenotypes such as weight, weight gain, egg production and egg quality measurements. Phenotypic data on 30 traits from two lines (L1, n=180; and L2, n=205) of quail were modeled to predict TEP. Prediction models included multiple linear regression and artificial neural network (ANN). Moreover, Bayesian network (BN) and a stepwise approach were used as variable selection methods. BN results showed that TEP is independent from other earlier expressed traits when conditioned on egg production from 35 to 80 days of age (EP1). In addition, the prediction accuracy was much lower when EP1 was not included in the model. The best predictive model was ANN, after feature selection, showing prediction correlations of r=0.792 and r=0.714 for L1 and L2, respectively. In conclusion, machine learning methods may be useful, but reasonable prediction accuracies are obtained only when partial egg production measurements are included in the model.
Assuntos
Criação de Animais Domésticos/métodos , Coturnix/fisiologia , Reprodução , Animais , Teorema de Bayes , Brasil , Modelos Biológicos , Redes Neurais de Computação , Análise de RegressãoRESUMO
We analyse data from a study involving 173 pregnant women. The data are observed values of the ß 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.