Error Autocorrelation and Linear Regression for Temperature-Based Evapotranspiration Estimates Improvement

Estimates of evapotranspiration on a local scale is important information for agricultural and hydrological practices. However, equations to estimate potential evapotranspiration based only on temperature data, which are simple to use, are usually less trustworthy than the Food and Agriculture Organization (FAO)Penman-Monteith standard method. The present work describes two correction procedures for potential evapotranspiration estimates by temperature, making the results more reliable. Initially, the standard FAO-Penman-Monteith method was evaluated with a complete climatologic data set for the period between 2002 and 2006. Then temperature-based estimates by Camargo and Jensen-Haise methods have been adjusted by error autocorrelation evaluated in biweekly and monthly periods. In a second adjustment, simple linear regression was applied. The adjusted equations have been validated with climatic data available for the Year 2001. Both proposed methodologies showed good agreement with the standard method indicating that the methodology can be used for local potential evapotranspiration estimates.

Generalized correlation functions for conductance fluctuations and the mesoscopic spin Hall effect

We study the spin Hall conductance fluctuations in ballistic mesoscopic systems. We obtain universal expressions for the spin and charge current fluctuations, cast in terms of current-current autocorrelation functions. We show that the latter are conveniently parametrized as deformed Lorentzian shape lines, functions of an external applied magnetic field and the Fermi energy. We find that the charge current fluctuations show quite unique statistical features at the symplectic-unitary crossover regime. Our findings are based on an evaluation of the generalized transmission coefficients correlation functions within the stub model and are amenable to experimental test. DOI: 10.1103/PhysRevB.86.235112

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.

Tráfego veicular e mortalidade por doenças do aparelho circulatório em homens adultos

OBJETIVO: Analisar a associação entre indicadores de exposição à poluição por tráfego veicular e mortalidade por doenças do aparelho circulatório em homens adultos. MÉTODOS: Foram analisadas informações sobre vias e volume de tráfego no ano de 2007 fornecidas pela companhia de engenharia de tráfego local. Mortalidade por doenças do aparelho circulatório no ano de 2005 entre homens ≥ 40 anos foram obtidas do registro de mortalidade do Programa de Aprimoramento de Informações de Mortalidade do Município de São Paulo, SP. Dados socioeconômicos do Censo 2000 e informações sobre a localização dos serviços de saúde também foram coletados. A exposição foi avaliada pela densidade de vias e volume de tráfego para cada distrito administrativo. Foi calculada regressão (α = 5%) entre esses indicadores de exposição e as taxas de mortalidade padronizadas, ajustando os modelos para variáveis socioeconômicas, número de serviços de saúde nos distritos e autocorrelação espacial. RESULTADOS: A correlação entre densidade de vias e volume de tráfego foi modesta (r² = 0,28). Os distritos do centro apresentaram os maiores valores de densidade de vias. O modelo de regressão espacial de densidade de vias indicou associação com mortalidade por doenças do aparelho circulatório (p = 0,017). Não se observou associação no modelo de volume de tráfego. Em ambos os modelos – vias e volume de tráfego (veículos leves/pesados) – a variável socioeconômica foi estatisticamente signifi cante. CONCLUSÕES: A associação entre mortalidade por doenças do aparelho circulatório e densidade de vias converge com a literatura e encoraja a realização de mais estudos epidemiológicos em nível individual e com métodos mais acurados de avaliação da exposição.

Influence diagnostics in heteroscedastic and/or autoregressive nonlinear elliptical models for correlated data

In this paper, we propose nonlinear elliptical models for correlated data with heteroscedastic and/or autoregressive structures. Our aim is to extend the models proposed by Russo et al. [22] by considering a more sophisticated scale structure to deal with variations in data dispersion and/or a possible autocorrelation among measurements taken throughout the same experimental unit. Moreover, to avoid the possible influence of outlying observations or to take into account the non-normal symmetric tails of the data, we assume elliptical contours for the joint distribution of random effects and errors, which allows us to attribute different weights to the observations. We propose an iterative algorithm to obtain the maximum-likelihood estimates for the parameters and derive the local influence curvatures for some specific perturbation schemes. The motivation for this work comes from a pharmacokinetic indomethacin data set, which was analysed previously by Bocheng and Xuping [1] under normality.

Lumpiness, capital adjustment costs and investment dynamics

Aggregate investment in the US economy displays a hump-shaped pattern in response to shocks, and the autocorrelation of aggregate investment growth is positive for the first few quarters, turning negative for the later quarters. This paper shows that this feature of the data is the natural outcome of a two-sector consumption/investment model designed and calibrated to reproduce plant-level evidence on capita: accumulation. (C) 2012 Elsevier B.V. All rights reserved.

Temporal relationships of a pulse of prolactin (PRL) to a pulse of a metabolite of PGF2 alpha in mares

Hourly blood samples were collected from 10 mares during 24 h of each of the preluteolytic, luteolytic, and postluteolytic periods. The autocorrelation function of the R program was used to detect pulse rhythmicity, and the intra-assay CV was used to locate and characterize pulses of prolactin (PRL) and a metabolite of prostaglandin F2 alpha (PGFM). Rhythmicity of PRL and PGFM concentrations was detected in 67% and 89% of mares, respectively. Combined for the three periods (no difference among periods), the PRL pulses were 5.2 +/- 0.4 h (mean +/- SEM) at the base, 7.5 +/- 1.5 h between nadirs of adjacent pulses, and 12.3 +/- 1.5 h from peak to peak. The peaks of PRL pulses were greater (P < 0.05) during the luteolytic period (46 +/- 14 ng/mL) and postluteolytic period (52 15 ng/mL) than during the preluteolytic period (17 3 ng/mL). Concentrations of PRL during hours of a PGFM pulse were different (P < 0.003) within the luteolytic period and postluteolytic period and were greatest at the PGFM peak; PRL concentrations during a PGFM pulse were not different during the preluteolytic period. The frequency of the peak of PRL and PGFM pulses occurring at the same hour (synchrony) was greater for the luteolytic period (65%, P < 0.01) and postluteolytic period (50%, P < 0.001) than for the preluteolytic period (17%). This is the first report in mares on characterization and rhythmicity of PRL pulses, synchrony between PRL and PGFM pulses, and greater PRL activity during the luteolytic and postluteolytic periods than during the preluteolytic period. (C) 2012 Elsevier Inc. All rights reserved.

Sensitivity, specificity and predictive values of linear and nonlinear indices of heart rate variability in stable angina patients

Abstract Background Decreased heart rate variability (HRV) is related to higher morbidity and mortality. In this study we evaluated the linear and nonlinear indices of the HRV in stable angina patients submitted to coronary angiography. Methods We studied 77 unselected patients for elective coronary angiography, which were divided into two groups: coronary artery disease (CAD) and non-CAD groups. For analysis of HRV indices, HRV was recorded beat by beat with the volunteers in the supine position for 40 minutes. We analyzed the linear indices in the time (SDNN [standard deviation of normal to normal], NN50 [total number of adjacent RR intervals with a difference of duration greater than 50ms] and RMSSD [root-mean square of differences]) and frequency domains ultra-low frequency (ULF) ≤ 0,003 Hz, very low frequency (VLF) 0,003 – 0,04 Hz, low frequency (LF) (0.04–0.15 Hz), and high frequency (HF) (0.15–0.40 Hz) as well as the ratio between LF and HF components (LF/HF). In relation to the nonlinear indices we evaluated SD1, SD2, SD1/SD2, approximate entropy (−ApEn), α1, α2, Lyapunov Exponent, Hurst Exponent, autocorrelation and dimension correlation. The definition of the cutoff point of the variables for predictive tests was obtained by the Receiver Operating Characteristic curve (ROC). The area under the ROC curve was calculated by the extended trapezoidal rule, assuming as relevant areas under the curve ≥ 0.650. Results Coronary arterial disease patients presented reduced values of SDNN, RMSSD, NN50, HF, SD1, SD2 and -ApEn. HF ≤ 66 ms2, RMSSD ≤ 23.9 ms, ApEn ≤−0.296 and NN50 ≤ 16 presented the best discriminatory power for the presence of significant coronary obstruction. Conclusion We suggest the use of Heart Rate Variability Analysis in linear and nonlinear domains, for prognostic purposes in patients with stable angina pectoris, in view of their overall impairment.