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.

Validating NCAR-CCSM last glacial maximum sea surface temperature in the tropical and South Atlantic with proxy-data

Last Glacial Maximum simulated sea surface temperature from the Paleo-Climate version of the National Center for Atmospheric Research Coupled Climate Model (NCAR-CCSM) are compared with available reconstructions and data-based products in the tropical and south Atlantic region. Model results are compared to data proxies based on the Multiproxy Approach for the Reconstruction of the Glacial Ocean surface product (MARGO). Results show that the model sea surface temperature is not consistent with the proxy-data in all of the region of interest. Discrepancies are found in the eastern, equatorial and in the high-latitude South Atlantic. The model overestimates the cooling in the southern South Atlantic (near 50 degrees S) shown by the proxy-data. Near the equator, model and proxies are in better agreement. In the eastern part of the equatorial basin the model underestimates the cooling shown by all proxies. A northward shift in the position of the subtropical convergence zone in the simulation suggests a compression or/and an equatorward shift of the subtropical gyre at the surface, consistent with what is observed in the proxy reconstruction. (C) 2008 Elsevier B.V. All rights reserved

A general threshold stress hybrid hazard model for lifetime data

In this paper we propose a hybrid hazard regression model with threshold stress which includes the proportional hazards and the accelerated failure time models as particular cases. To express the behavior of lifetimes the generalized-gamma distribution is assumed and an inverse power law model with a threshold stress is considered. For parameter estimation we develop a sampling-based posterior inference procedure based on Markov Chain Monte Carlo techniques. We assume proper but vague priors for the parameters of interest. A simulation study investigates the frequentist properties of the proposed estimators obtained under the assumption of vague priors. Further, some discussions on model selection criteria are given. The methodology is illustrated on simulated and real lifetime data set.

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.

Bayesian analysis of weak gravitational lensing and Sunyaev-Zel'dovich data for six galaxy clusters

We present an analysis of observations made with the Arcminute Microkelvin Imager (AMI) and the CanadaFranceHawaii Telescope (CFHT) of six galaxy clusters in a redshift range of 0.160.41. The cluster gas is modelled using the SunyaevZeldovich (SZ) data provided by AMI, while the total mass is modelled using the lensing data from the CFHT. In this paper, we (i) find very good agreement between SZ measurements (assuming large-scale virialization and a gas-fraction prior) and lensing measurements of the total cluster masses out to r200; (ii) perform the first multiple-component weak-lensing analysis of A115; (iii) confirm the unusual separation between the gas and mass components in A1914 and (iv) jointly analyse the SZ and lensing data for the relaxed cluster A611, confirming our use of a simulation-derived masstemperature relation for parametrizing measurements of the SZ effect.

Climatic oscillations shape the phylogeographical structure of Atlantic Forest fire-eye antbirds (Aves: Thamnophilidae)

In the present study, mitochondrial (mt)DNA sequence data were used to examine the genetic structure of fire-eye antbirds (genus Pyriglena) along the Atlantic Forest and the predictions derived from the river hypothesis and from a Last Glacial Maximum Pleistocene refuge paleomodel were compared to explain the patterns of genetic variation observed in these populations. A total of 266 individuals from 45 populations were sampled over a latitudinal transect and a number of phylogeographical and population genetics analytical approaches were employed to address these questions. The pattern of mtDNA variation observed in fire-eye antbirds provides little support for the view that populations were isolated by the modern course of major Atlantic Forest rivers. Instead, the data provide stronger support for the predictions of the refuge model. These results add to the mounting evidence that climatic oscillations appear to have played a substantial role in shaping the phylogeographical structure and possibly the diversification of many taxa in this region. However, the results also illustrate the potential for more complex climatic history and historical changes in the geographical distribution of Atlantic Forest than envisioned by the refuge model. (c) 2012 The Linnean Society of London, Biological Journal of the Linnean Society, 2012, 105, 900824.

An overview of biodiesel soil pollution: Data based on cytotoxicity and genotoxicity assessments

Biodiesel production has received considerable attention in the recent past as a nonpolluting fuel. However, this assertion has been based on its biodegradability and reduction in exhaust emissions. Assessments of water and soil biodiesel pollution are still limited. Spill simulation with biodiesel and their diesel blends in soils were carried out, aiming at analyzing their cytotoxic and genotoxic potentials. While the cytotoxicity observed may be related to diesel contaminants, the genotoxic and mutagenic effects can be ascribed to biodiesel pollutants. Thus, taking into account that our data stressed harmful effects on organisms exposed to biodiesel-polluted soils, the designation of this biofuel as an environmental-friendly fuel should be carefully reviewed to assure environmental quality. (C) 2011 Elsevier B.V. All rights reserved.

Stochastic simulation of time-series models combined with geostatistics to predict water-table scenarios in a Guarani Aquifer System outcrop area, Brazil

Stochastic methods based on time-series modeling combined with geostatistics can be useful tools to describe the variability of water-table levels in time and space and to account for uncertainty. Monitoring water-level networks can give information about the dynamic of the aquifer domain in both dimensions. Time-series modeling is an elegant way to treat monitoring data without the complexity of physical mechanistic models. Time-series model predictions can be interpolated spatially, with the spatial differences in water-table dynamics determined by the spatial variation in the system properties and the temporal variation driven by the dynamics of the inputs into the system. An integration of stochastic methods is presented, based on time-series modeling and geostatistics as a framework to predict water levels for decision making in groundwater management and land-use planning. The methodology is applied in a case study in a Guarani Aquifer System (GAS) outcrop area located in the southeastern part of Brazil. Communication of results in a clear and understandable form, via simulated scenarios, is discussed as an alternative, when translating scientific knowledge into applications of stochastic hydrogeology in large aquifers with limited monitoring network coverage like the GAS.

The log-exponentiated generalized gamma regression model for censored data

For the first time, we introduce a generalized form of the exponentiated generalized gamma distribution [Cordeiro et al. The exponentiated generalized gamma distribution with application to lifetime data, J. Statist. Comput. Simul. 81 (2011), pp. 827-842.] that is the baseline for the log-exponentiated generalized gamma regression model. The new distribution can accommodate increasing, decreasing, bathtub- and unimodal-shaped hazard functions. A second advantage is that it includes classical distributions reported in the lifetime literature as special cases. We obtain explicit expressions for the moments of the baseline distribution of the new regression model. The proposed model can be applied to censored data since it includes as sub-models several widely known regression models. It therefore can be used more effectively in the analysis of survival data. We obtain maximum likelihood estimates for the model parameters by considering censored data. We show that our extended regression model is very useful by means of two applications to real data.

Simulation results and applications of an advection bounded scheme to practical flows

This paper reports experiments on the use of a recently introduced advection bounded upwinding scheme, namely TOPUS (Computers & Fluids 57 (2012) 208-224), for flows of practical interest. The numerical results are compared against analytical, numerical and experimental data and show good agreement with them. It is concluded that the TOPUS scheme is a competent, powerful and generic scheme for complex flow phenomena.

Utility Harmonic Impedance Measurement Based on Data Selection

Determination of the utility harmonic impedance based on measurements is a significant task for utility power-quality improvement and management. Compared to those well-established, accurate invasive methods, the noninvasive methods are more desirable since they work with natural variations of the loads connected to the point of common coupling (PCC), so that no intentional disturbance is needed. However, the accuracy of these methods has to be improved. In this context, this paper first points out that the critical problem of the noninvasive methods is how to select the measurements that can be used with confidence for utility harmonic impedance calculation. Then, this paper presents a new measurement technique which is based on the complex data-based least-square regression, combined with two techniques of data selection. Simulation and field test results show that the proposed noninvasive method is practical and robust so that it can be used with confidence to determine the utility harmonic impedances.

A Bayesian Approach for Decision Making on the Identification of Genes with Different Expression Levels: An Application to Escherichia coli Bacterium Data

A common interest in gene expression data analysis is to identify from a large pool of candidate genes the genes that present significant changes in expression levels between a treatment and a control biological condition. Usually, it is done using a statistic value and a cutoff value that are used to separate the genes differentially and nondifferentially expressed. In this paper, we propose a Bayesian approach to identify genes differentially expressed calculating sequentially credibility intervals from predictive densities which are constructed using the sampled mean treatment effect from all genes in study excluding the treatment effect of genes previously identified with statistical evidence for difference. We compare our Bayesian approach with the standard ones based on the use of the t-test and modified t-tests via a simulation study, using small sample sizes which are common in gene expression data analysis. Results obtained report evidence that the proposed approach performs better than standard ones, especially for cases with mean differences and increases in treatment variance in relation to control variance. We also apply the methodologies to a well-known publicly available data set on Escherichia coli bacterium.

On the impact of disproportional samples in credit scoring models: An application to a Brazilian bank data

Statistical methods have been widely employed to assess the capabilities of credit scoring classification models in order to reduce the risk of wrong decisions when granting credit facilities to clients. The predictive quality of a classification model can be evaluated based on measures such as sensitivity, specificity, predictive values, accuracy, correlation coefficients and information theoretical measures, such as relative entropy and mutual information. In this paper we analyze the performance of a naive logistic regression model (Hosmer & Lemeshow, 1989) and a logistic regression with state-dependent sample selection model (Cramer, 2004) applied to simulated data. Also, as a case study, the methodology is illustrated on a data set extracted from a Brazilian bank portfolio. Our simulation results so far revealed that there is no statistically significant difference in terms of predictive capacity between the naive logistic regression models and the logistic regression with state-dependent sample selection models. However, there is strong difference between the distributions of the estimated default probabilities from these two statistical modeling techniques, with the naive logistic regression models always underestimating such probabilities, particularly in the presence of balanced samples. (C) 2012 Elsevier Ltd. All rights reserved.

Modified crop model estimation of depleted and potential soybean yield

Despite the great importance of soybeans in Brazil, there have been few applications of soybean crop modeling on Brazilian conditions. Thus, the objective of this study was to use modified crop models to estimate the depleted and potential soybean crop yield in Brazil. The climatic variable data used in the modified simulation of the soybean crop models were temperature, insolation and rainfall. The data set was taken from 33 counties (28 Sao Paulo state counties, and 5 counties from other states that neighbor São Paulo). Among the models, modifications in the estimation of the leaf area of the soybean crop, which includes corrections for the temperature, shading, senescence, CO2, and biomass partition were proposed; also, the methods of input for the model's simulation of the climatic variables were reconsidered. The depleted yields were estimated through a water balance, from which the depletion coefficient was estimated. It can be concluded that the adaptation soybean growth crop model might be used to predict the results of the depleted and potential yield of soybeans, and it can also be used to indicate better locations and periods of tillage.

Computational simulation of the flow-induced vibration of a circular cylinder subjected to wake interference

In this work, we considered the flow around two circular cylinders of equal diameter placed in tandem with respect to the incident uniform flow. The upstream cylinder was fixed and the downstream cylinder was completely free to move in the cross-stream direction, with no spring or damper attached to it. The centre-to-centre distance between the cylinders was four diameters, and the Reynolds number was varied from 100 to 645. We performed two- and three-dimensional simulations of this flow using a Spectral/hp element method to discretise the flow equations, coupled to a simple Newmark integration routine that solves the equation of the dynamics of the cylinder. The differences of the behaviours observed in the two- and three-dimensional simulations are highlighted and the data is analysed under the light of previously published experimental results obtained for higher Reynolds numbers.