Infant mortality in Brazil, 1980-2000: A spatial panel data analysis

Background: Infant mortality is an important measure of human development, related to the level of welfare of a society. In order to inform public policy, various studies have tried to identify the factors that influence, at an aggregated level, infant mortality. The objective of this paper is to analyze the regional pattern of infant mortality in Brazil, evaluating the effect of infrastructure, socio-economic, and demographic variables to understand its distribution across the country. Methods: Regressions including socio-economic and living conditions variables are conducted in a structure of panel data. More specifically, a spatial panel data model with fixed effects and a spatial error autocorrelation structure is used to help to solve spatial dependence problems. The use of a spatial modeling approach takes into account the potential presence of spillovers between neighboring spatial units. The spatial units considered are Minimum Comparable Areas, defined to provide a consistent definition across Census years. Data are drawn from the 1980, 1991 and 2000 Census of Brazil, and from data collected by the Ministry of Health (DATASUS). In order to identify the influence of health care infrastructure, variables related to the number of public and private hospitals are included. Results: The results indicate that the panel model with spatial effects provides the best fit to the data. The analysis confirms that the provision of health care infrastructure and social policy measures (e. g. improving education attainment) are linked to reduced rates of infant mortality. An original finding concerns the role of spatial effects in the analysis of IMR. Spillover effects associated with health infrastructure and water and sanitation facilities imply that there are regional benefits beyond the unit of analysis. Conclusions: A spatial modeling approach is important to produce reliable estimates in the analysis of panel IMR data. Substantively, this paper contributes to our understanding of the physical and social factors that influence IMR in the case of a developing country.

THE LOG-BURR XII REGRESSION MODEL FOR GROUPED SURVIVAL DATA

The log-Burr XII regression model for grouped survival data is evaluated in the presence of many ties. The methodology for grouped survival data is based on life tables, where the times are grouped in k intervals, and we fit discrete lifetime regression models to the data. The model parameters are estimated by maximum likelihood and jackknife methods. To detect influential observations in the proposed model, diagnostic measures based on case deletion, so-called global influence, and influence measures based on small perturbations in the data or in the model, referred to as local influence, are used. In addition to these measures, the total local influence and influential estimates are also used. We conduct Monte Carlo simulation studies to assess the finite sample behavior of the maximum likelihood estimators of the proposed model for grouped survival. A real data set is analyzed using a regression model for grouped data.

Study of using marker assisted selection on a beef cattle breeding program by model comparison

A data set of a commercial Nellore beef cattle selection program was used to compare breeding models that assumed or not markers effects to estimate the breeding values, when a reduced number of animals have phenotypic, genotypic and pedigree information available. This herd complete data set was composed of 83,404 animals measured for weaning weight (WW), post-weaning gain (PWG), scrotal circumference (SC) and muscle score (MS), corresponding to 116,652 animals in the relationship matrix. Single trait analyses were performed by MTDFREML software to estimate fixed and random effects solutions using this complete data. The additive effects estimated were assumed as the reference breeding values for those animals. The individual observed phenotype of each trait was adjusted for fixed and random effects solutions, except for direct additive effects. The adjusted phenotype composed of the additive and residual parts of observed phenotype was used as dependent variable for models' comparison. Among all measured animals of this herd, only 3160 animals were genotyped for 106 SNP markers. Three models were compared in terms of changes on animals' rank, global fit and predictive ability. Model 1 included only polygenic effects, model 2 included only markers effects and model 3 included both polygenic and markers effects. Bayesian inference via Markov chain Monte Carlo methods performed by TM software was used to analyze the data for model comparison. Two different priors were adopted for markers effects in models 2 and 3, the first prior assumed was a uniform distribution (U) and, as a second prior, was assumed that markers effects were distributed as normal (N). Higher rank correlation coefficients were observed for models 3_U and 3_N, indicating a greater similarity of these models animals' rank and the rank based on the reference breeding values. Model 3_N presented a better global fit, as demonstrated by its low DIC. The best models in terms of predictive ability were models 1 and 3_N. Differences due prior assumed to markers effects in models 2 and 3 could be attributed to the better ability of normal prior in handle with collinear effects. The models 2_U and 2_N presented the worst performance, indicating that this small set of markers should not be used to genetically evaluate animals with no data, since its predictive ability is restricted. In conclusion, model 3_N presented a slight superiority when a reduce number of animals have phenotypic, genotypic and pedigree information. It could be attributed to the variation retained by markers and polygenic effects assumed together and the normal prior assumed to markers effects, that deals better with the collinearity between markers. (C) 2012 Elsevier B.V. All rights reserved.

Análise de concorrência no mercado mundial de café verde

A literatura argumenta que o Brasil, embora ainda seja o maior exportador mundial de café verde, tem perdido poder neste mercado, pois a concorrência (rivalidade e probabilidade de entrada) imposta por países como a Colômbia e o Vietnã é forte o suficiente para tornar este mercado bastante competitivo. Assim, este artigo avalia o padrão recente de concorrência do mercado mundial de café verde utilizando uma metodologia econométrica mais usualmente empregada em análise antitruste. Para avaliar o comportamento dos consumidores, foram estimadas as elasticidades-preço da demanda mundial de café verde, por tipo de café, usando o modelo de demanda Logit Multinomial Antitruste. Para avaliar o comportamento de equilíbrio de mercado foram realizados testes de instabilidade de share de quantidade por meio de análise de cointegração em painel. Os resultados apontam para aumento da concorrência à variedade de café brasileiro por parte da demanda e manutenção de sharede quantidades como configuração de equilíbrio de mercado.

Coassimetria, cocurtose e as taxas de retorno das ações: uma análise com dados em painel

Modelos de apreçamento de ativos têm sido um tema sob constante investigação em finanças. Desde o capital asset pricing model (CAPM) proposto por Sharpe (1964), tais modelos relacionam, geralmente de maneira linear, a taxa de retorno esperada de um ativo ou carteira de ativos com fatores de risco sistêmico. Esta pesquisa apresenta um teste de um modelo de apreçamento, com dados brasileiros, introduzindo em sua formulação fatores de risco baseados em comomentos estatísticos. O modelo proposto é uma extensão do CAPM original acrescido da coassimetria e da cocurtose entre as taxas de retorno das ações das empresas que compõem a amostra e as taxas de retorno da carteira de mercado. Os efeitos de outras variáveis, como o valor de mercado sobre valor contábil, a alavancagem financeira e um índice de negociabilidade em bolsa, serviram de variáveis de controle. A amostra foi composta de 179 empresas brasileiras não financeiras negociadas na BM&FBovespa e com dados disponíveis entre os anos de 2003 a 2007. A metodologia consistiu em calcular os momentos sistêmicos anuais a partir de taxas de retornos semanais e em seguida testá-los em um modelo de apreçamento, a fim de verificar se há um prêmio pelo risco associado a cada uma dessas medidas de risco. Foi empregada a técnica de análise de dados em painel, estimada pelo método dos momentos generalizado (GMM). O emprego do GMM visa lidar com potenciais problemas de determinação simultânea e endogeneidade nos dados, evitando a ocorrência de viés nas estimações. Os resultados das estimações mostram que a relação das taxas de retorno dos ativos com a covariância e a cocurtose são estatisticamente significantes. Os resultados se mostraram robustos a especificações alternativas do modelo. O artigo contribui para a literatura por apresentar evidências empíricas brasileiras de que há um prêmio pelo risco associado aos momentos sistêmicos.

Discretionary-based disclosure: evidence from the Brazilian market

The primary objective of this paper is to identify the factors that explain Brazilian companies level of voluntary disclosure. Underpinning this work is the Discretionary-based Disclosure theory. The sample is composed of the top 100 largest non-financial companies listed in the Bolsa de Valores de São Paulo (Brazilian Securities, Commodities, and Futures exchange - BOVESPA). Information was gathered from Financial Statements for the years ending in 2006, 2007, and 2008, with the use of content analysis. A disclosure framework based on 27 studies from these years was created, with a total of 92 voluntary items divided into two dimensions: economic (43) and socio-environmental (49). Based on the existing literature, a total of 12 hypotheses were elaborated and tested using a panel data approach. Results evidence that: (a) Sector and Origin of Control are statistically significant in all three models tested: economic, socio-environmental, and total; (b) Profitability is relevant in the economic model and in the total model; (c) Tobin s Q is relevant in the socio-environmental model and in the total disclosure model; (d) Leverage and Auditing Firm are only relevant in the economic disclosure model; (e) Size, Governance, Stock Issuing, Growth Opportunities and Concentration of Control are not statistically significant in any of the three models.

Characterization of depolymerized residues from extremely low acid hydrolysis (ELA) of sugarcane bagasse cellulose: Effects of degree of polymerization, crystallinity and crystallite size on thermal decomposition

Sugarcane bagasse cellulose was subjected to the extremely low acid (ELA) hydrolysis in 0.07% H2SO4 at 190, 210 and 225 degrees C for various times. The cellulose residues from this process were characterized by TGA, XRD, GPC, FIR and SEM. A kinetic study of thermal decomposition of the residues was also carried out, using the ASTM and Kissinger methods. The thermal studies revealed that residues of cellulose hydrolyzed at 190, 210 and 225 degrees C for 80,40 and 8 min have initial decomposition temperature and activation energy for the main decomposition step similar to those of Avicel PH-101. XRD studies confirmed this finding by showing that these cellulose residues are similar to Avicel in crystallinity index and crystallite size in relation to the 110 and 200 planes. FTIR spectra revealed no significant changes in the cellulose chemical structure and analysis of SEM micrographs demonstrated that the particle size of the cellulose residues hydrolyzed at 190 and 210 degrees C were similar to that of Avicel. (C) 2011 Elsevier B.V. All rights reserved.

Employment Effects of Product and Labour Market Reforms: Are There Synergies?

This article investigates the effect of product market liberalisation on employment allowing for interactions between policies and institutions in product and labour markets. Using panel data for OECD countries over the period 19802002, we present evidence that product market deregulation is more effective at the margin when labour market regulation is high. The data also suggest that product market liberalisation may promote employment-enhancing labour market reforms.

Covariates of high-risk human papillomavirus (HPV) infections are distinct for incident CIN1, CIN2 and CIN3 as disclosed by competing-risks regression models

Background: In addition to the oncogenic human papillomavirus (HPV), several cofactors are needed in cervical carcinogenesis, but whether the HPV covariates associated with incident i) CIN1 are different from those of incident ii) CIN2 and iii) CIN3 needs further assessment. Objectives: To gain further insights into the true biological differences between CIN1, CIN2 and CIN3, we assessed HPV covariates associated with incident CIN1, CIN2, and CIN3. Study Design and Methods: HPV covariates associated with progression to CIN1, CIN2 and CIN3 were analysed in the combined cohort of the NIS (n = 3,187) and LAMS study (n = 12,114), using competing-risks regression models (in panel data) for baseline HR-HPV-positive women (n = 1,105), who represent a sub-cohort of all 1,865 women prospectively followed-up in these two studies. Results: Altogether, 90 (4.8%), 39 (2.1%) and 14 (1.4%) cases progressed to CIN1, CIN2, and CIN3, respectively. Among these baseline HR-HPV-positive women, the risk profiles of incident GIN I, CIN2 and CIN3 were unique in that completely different HPV covariates were associated with progression to CIN1, CIN2 and CIN3, irrespective which categories (non-progression, CIN1, CIN2, CIN3 or all) were used as competing-risks events in univariate and multivariate models. Conclusions: These data confirm our previous analysis based on multinomial regression models implicating that distinct covariates of HR-HPV are associated with progression to CIN1, CIN2 and CIN3. This emphasises true biological differences between the three grades of GIN, which revisits the concept of combining CIN2 with CIN3 or with CIN1 in histological classification or used as a common end-point, e.g., in HPV vaccine trials.

TUNGSTEN ISOTOPIC COMPOSITIONS IN STARDUST SiC GRAINS FROM THE MURCHISON METEORITE: CONSTRAINTS ON THE s-PROCESS IN THE Hf-Ta-W-Re-Os REGION

We report the first tungsten isotopic measurements in stardust silicon carbide (SiC) grains recovered from the Murchison carbonaceous chondrite. The isotopes (182,183,184,186)Wand (179,180)Hf were measured on both an aggregate (KJB fraction) and single stardust SiC grains (LS+ LU fraction) believed to have condensed in the outflows of low-mass carbon-rich asymptotic giant branch (AGB) stars with close-to-solar metallicity. The SiC aggregate shows small deviations from terrestrial (= solar) composition in the (182)W/(184)Wand (183)W/(184)Wratios, with deficits in (182)W and (183)W with respect to (184)W. The (186)W/(184)W ratio, however, shows no apparent deviation from the solar value. Tungsten isotopic measurements in single mainstream stardust SiC grains revealed lower than solar (182)W/(184)W, (183)W/(184)W, and (186)W/(184)W ratios. We have compared the SiC data with theoretical predictions of the evolution of W isotopic ratios in the envelopes of AGB stars. These ratios are affected by the slow neutron-capture process and match the SiC data regarding their (182)W/(184)W, (183)W/(184)W, and (179)Hf/(180)Hf isotopic compositions, although a small adjustment in the s-process production of (183)W is needed in order to have a better agreement between the SiC data and model predictions. The models cannot explain the (186)W/(184)W ratios observed in the SiC grains, even when the current (185)W neutron-capture cross section is increased by a factor of two. Further study is required to better assess how model uncertainties (e. g., the formation of the (13)C neutron source, the mass-loss law, the modeling of the third dredge-up, and the efficiency of the (22)Ne neutron source) may affect current s-process predictions.

Network-based stochastic semisupervised learning

Semisupervised learning is a machine learning approach that is able to employ both labeled and unlabeled samples in the training process. In this paper, we propose a semisupervised data classification model based on a combined random-preferential walk of particles in a network (graph) constructed from the input dataset. The particles of the same class cooperate among themselves, while the particles of different classes compete with each other to propagate class labels to the whole network. A rigorous model definition is provided via a nonlinear stochastic dynamical system and a mathematical analysis of its behavior is carried out. A numerical validation presented in this paper confirms the theoretical predictions. An interesting feature brought by the competitive-cooperative mechanism is that the proposed model can achieve good classification rates while exhibiting low computational complexity order in comparison to other network-based semisupervised algorithms. Computer simulations conducted on synthetic and real-world datasets reveal the effectiveness of the model.

Multi-strange baryon production in pp collisions at root s=7 TeV with ALICE

A measurement of the multi-strange Xi(-) and Omega(-) baryons and their antiparticles by the ALICE experiment at the CERN Large Hadron Collider (LHC) is presented for inelastic proton-proton collisions at a centre-of-mass energy of 7 TeV. The transverse momentum (p(T)) distributions were studied at mid-rapidity (vertical bar y vertical bar < 0.5) in the range of 0.6 < p(T) < 8.5 GeV/c Xi(-) for and Xi(+) baryons, and in the range of 0.8 < P-T < 5 GeV/c for Omega(-) and<(Omega)over bar>(+). Baryons and antibaryons were measured as separate particles and we find that the baryon to antibaryon ratio of both particle species is consistent with unity over the entire range of the measurement. The statistical precision of the current data has allowed us to measure a difference between the mean p(T) of Xi(-) ((Xi) over bar)(+) and Omega(-) ((Omega) over bar (+)). Particle yields, mean pi, and the spectra in the intermediate pi range are not well described by the PYTHIA Perugia 2011 tune Monte Carlo event generator, which has been tuned to reproduce the early LHC data. The discrepancy is largest for Omega(-)((Omega) over bar (+)). This PYTHIA tune approaches the pi spectra of Xi(-) and Xi(+) baryons below p(T) <0.85 GeV/c and describes the Xi(-) and Xi(+) spectra above p(T) > 6.0 GeV/c. We also illustrate the difference between the experimental data and model by comparing the corresponding ratios of (Omega(-) +(Omega) over bar (+))/(Xi(-) + Xi(+)) as a function of transverse mass. (C) 2012 CERN. Published by Elsevier B.V. All rights reserved.