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.

Approximate decoding approaches for network coded correlated data

This paper considers a framework where data from correlated sources are transmitted with the help of network coding in ad hoc network topologies. The correlated data are encoded independently at sensors and network coding is employed in the intermediate nodes in order to improve the data delivery performance. In such settings, we focus on the problem of reconstructing the sources at decoder when perfect decoding is not possible due to losses or bandwidth variations. We show that the source data similarity can be used at decoder to permit decoding based on a novel and simple approximate decoding scheme. We analyze the influence of the network coding parameters and in particular the size of finite coding fields on the decoding performance. We further determine the optimal field size that maximizes the expected decoding performance as a trade-off between information loss incurred by limiting the resolution of the source data and the error probability in the reconstructed data. Moreover, we show that the performance of the approximate decoding improves when the accuracy of the source model increases even with simple approximate decoding techniques. We provide illustrative examples showing how the proposed algorithm can be deployed in sensor networks and distributed imaging applications.

Economic design of an X chart for short-run production

The aim of this paper is to present an economical design of an X chart for a short-run production. The process mean starts equal to mu(0) (in-control, State I) and in a random time it shifts to mu(1) > mu(0) (out-of-control, State II). The monitoring procedure consists of inspecting a single item at every m produced ones. If the measurement of the quality characteristic does not meet the control limits, the process is stopped, adjusted, and additional (r - 1) items are inspected retrospectively. The probabilistic model was developed considering only shifts in the process mean. A direct search technique is applied to find the optimum parameters which minimizes the expected cost function. Numerical examples illustrate the proposed procedure. (C) 2009 Elsevier B.V. All rights reserved.

Detection of quasi-periodic processes in complex systems: how do we quantitatively describe their properties?

It has been shown that in reality at least two general scenarios of data structuring are possible: (a) a self-similar (SS) scenario when the measured data form an SS structure and (b) a quasi-periodic (QP) scenario when the repeated (strongly correlated) data form random sequences that are almost periodic with respect to each other. In the second case it becomes possible to describe their behavior and express a part of their randomness quantitatively in terms of the deterministic amplitude–frequency response belonging to the generalized Prony spectrum. This possibility allows us to re-examine the conventional concept of measurements and opens a new way for the description of a wide set of different data. In particular, it concerns different complex systems when the ‘best-fit’ model pretending to be the description of the data measured is absent but the barest necessity of description of these data in terms of the reduced number of quantitative parameters exists. The possibilities of the proposed approach and detection algorithm of the QP processes were demonstrated on actual data: spectroscopic data recorded for pure water and acoustic data for a test hole. The suggested methodology allows revising the accepted classification of different incommensurable and self-affine spatial structures and finding accurate interpretation of the generalized Prony spectroscopy that includes the Fourier spectroscopy as a partial case.

DESARROLLO DE APLICACIONES ESTADÍSTICAS PARA LA AGRICULTURA DE PRECISIÓN. STATISTICAL APPLICATIONS FOR PRECISION AGRICULTURE

A partir de las últimas décadas se ha impulsado el desarrollo y la utilización de los Sistemas de Información Geográficos (SIG) y los Sistemas de Posicionamiento Satelital (GPS) orientados a mejorar la eficiencia productiva de distintos sistemas de cultivos extensivos en términos agronómicos, económicos y ambientales. Estas nuevas tecnologías permiten medir variabilidad espacial de propiedades del sitio como conductividad eléctrica aparente y otros atributos del terreno así como el efecto de las mismas sobre la distribución espacial de los rendimientos. Luego, es posible aplicar el manejo sitio-específico en los lotes para mejorar la eficiencia en el uso de los insumos agroquímicos, la protección del medio ambiente y la sustentabilidad de la vida rural. En la actualidad, existe una oferta amplia de recursos tecnológicos propios de la agricultura de precisión para capturar variación espacial a través de los sitios dentro del terreno. El óptimo uso del gran volumen de datos derivado de maquinarias de agricultura de precisión depende fuertemente de las capacidades para explorar la información relativa a las complejas interacciones que subyacen los resultados productivos. La covariación espacial de las propiedades del sitio y el rendimiento de los cultivos ha sido estudiada a través de modelos geoestadísticos clásicos que se basan en la teoría de variables regionalizadas. Nuevos desarrollos de modelos estadísticos contemporáneos, entre los que se destacan los modelos lineales mixtos, constituyen herramientas prometedoras para el tratamiento de datos correlacionados espacialmente. Más aún, debido a la naturaleza multivariada de las múltiples variables registradas en cada sitio, las técnicas de análisis multivariado podrían aportar valiosa información para la visualización y explotación de datos georreferenciados. La comprensión de las bases agronómicas de las complejas interacciones que se producen a la escala de lotes en producción, es hoy posible con el uso de éstas nuevas tecnologías. Los objetivos del presente proyecto son: (l) desarrollar estrategias metodológicas basadas en la complementación de técnicas de análisis multivariados y geoestadísticas, para la clasificación de sitios intralotes y el estudio de interdependencias entre variables de sitio y rendimiento; (ll) proponer modelos mixtos alternativos, basados en funciones de correlación espacial de los términos de error que permitan explorar patrones de correlación espacial de los rendimientos intralotes y las propiedades del suelo en los sitios delimitados. From the last decades the use and development of Geographical Information Systems (GIS) and Satellite Positioning Systems (GPS) is highly promoted in cropping systems. Such technologies allow measuring spatial variability of site properties including electrical conductivity and others soil features as well as their impact on the spatial variability of yields. Therefore, site-specific management could be applied to improve the efficiency in the use of agrochemicals, the environmental protection, and the sustainability of the rural life. Currently, there is a wide offer of technological resources to capture spatial variation across sites within field. However, the optimum use of data coming from the precision agriculture machineries strongly depends on the capabilities to explore the information about the complex interactions underlying the productive outputs. The covariation between spatial soil properties and yields from georeferenced data has been treated in a graphical manner or with standard geostatistical approaches. New statistical modeling capabilities from the Mixed Linear Model framework are promising to deal with correlated data such those produced by the precision agriculture. Moreover, rescuing the multivariate nature of the multiple data collected at each site, several multivariate statistical approaches could be crucial tools for data analysis with georeferenced data. Understanding the basis of complex interactions at the scale of production field is now within reach the use of these new techniques. Our main objectives are: (1) to develop new statistical strategies, based on the complementarities of geostatistics and multivariate methods, useful to classify sites within field grown with grain crops and analyze the interrelationships of several soil and yield variables, (2) to propose mixed linear models to predict yield according spatial soil variability and to build contour maps to promote a more sustainable agriculture.

Spatial statistical analysis and selection of genotypes in plant breeding

The objective of this study was to evaluate the efficiency of spatial statistical analysis in the selection of genotypes in a plant breeding program and, particularly, to demonstrate the benefits of the approach when experimental observations are not spatially independent. The basic material of this study was a yield trial of soybean lines, with five check varieties (of fixed effect) and 110 test lines (of random effects), in an augmented block design. The spatial analysis used a random field linear model (RFML), with a covariance function estimated from the residuals of the analysis considering independent errors. Results showed a residual autocorrelation of significant magnitude and extension (range), which allowed a better discrimination among genotypes (increase of the power of statistical tests, reduction in the standard errors of estimates and predictors, and a greater amplitude of predictor values) when the spatial analysis was applied. Furthermore, the spatial analysis led to a different ranking of the genetic materials, in comparison with the non-spatial analysis, and a selection less influenced by local variation effects was obtained.

Sampling in chemical analysis

The uncertainty of any analytical determination depends on analysis and sampling. Uncertainty arising from sampling is usually not controlled and methods for its evaluation are still little known. Pierre Gy’s sampling theory is currently the most complete theory about samplingwhich also takes the design of the sampling equipment into account. Guides dealing with the practical issues of sampling also exist, published by international organizations such as EURACHEM, IUPAC (International Union of Pure and Applied Chemistry) and ISO (International Organization for Standardization). In this work Gy’s sampling theory was applied to several cases, including the analysis of chromite concentration estimated on SEM (Scanning Electron Microscope) images and estimation of the total uncertainty of a drug dissolution procedure. The results clearly show that Gy’s sampling theory can be utilized in both of the above-mentioned cases and that the uncertainties achieved are reliable. Variographic experiments introduced in Gy’s sampling theory are beneficially applied in analyzing the uncertainty of auto-correlated data sets such as industrial process data and environmental discharges. The periodic behaviour of these kinds of processes can be observed by variographic analysis as well as with fast Fourier transformation and auto-correlation functions. With variographic analysis, the uncertainties are estimated as a function of the sampling interval. This is advantageous when environmental data or process data are analyzed as it can be easily estimated how the sampling interval is affecting the overall uncertainty. If the sampling frequency is too high, unnecessary resources will be used. On the other hand, if a frequency is too low, the uncertainty of the determination may be unacceptably high. Variographic methods can also be utilized to estimate the uncertainty of spectral data produced by modern instruments. Since spectral data are multivariate, methods such as Principal Component Analysis (PCA) are needed when the data are analyzed. Optimization of a sampling plan increases the reliability of the analytical process which might at the end have beneficial effects on the economics of chemical analysis,

Análise de resíduos em modelos de tempo de falha acelerado com efeito aleatório

We present residual analysis techniques to assess the fit of correlated survival data by Accelerated Failure Time Models (AFTM) with random effects. We propose an imputation procedure for censored observations and consider three types of residuals to evaluate different model characteristics. We illustrate the proposal with the analysis of AFTM with random effects to a real data set involving times between failures of oil well equipment

Modelagem do clima de ondas e seus efeitos sobre as feições morfológicas costeiras no litoral setentrional do Rio Grande do Norte

This thesis presents the results of application of SWAN Simulating WAves Nearshore numerical model, OF third generation, which simulates the propagation and dissipation of energy from sea waves, on the north continental shelf at Rio Grande do Norte, to determine the wave climate, calibrate and validate the model, and assess their potential and limitations for the region of interest. After validation of the wave climate, the results were integrated with information from the submarine relief, and plant morphology of beaches and barrier islands systems. On the second phase, the objective was to analyze the evolution of the wave and its interaction with the shallow seabed, from three transverse profiles orientation from N to S, distributed according to the parallel longitudinal, X = 774000-W, 783000-W e 800000-W. Subsequently, it was were extracted the values of directional waves and winds through all the months between november 2010 to november 2012, to analyze the impact of these forces on the movement area, and then understand the behavior of the morphological variations according to temporal year variability. Based on the results of modeling and its integration with correlated data, and planimetric variations of Soledade and Minhoto beach systems and Ponta do Tubarão and Barra do Fernandes barrier islands systems, it was obtained the following conclusions: SWAN could reproduce and determine the wave climate on the north continental shelf at RN, the results show a similar trend for the measurements of temporal variations of significant height (HS, m) and the mean wave period (Tmed, s); however, the results of parametric statistics were low for the estimates of the maximum values in most of the analyzed periods compared data of PT 1 and PT 2 (measurement points), with alternation of significant wave heights, at times overrated with occasional overlap of swell episodes. By analyzing the spatial distribution of the wave climate and its interaction with the underwater compartmentalization, it was concluded that there is interaction of wave propagation with the seafloor, showing change in significant heights whenever it interacts with the seafloor features (beachrocks, symmetric and asymmetric longitudinal dunes, paleochannel, among others) in the regions of outer, middle and inner shelf. And finally, it is concluded that the study of the stability areas allows identifications of the most unstable regions, confirming that the greatest range of variation indicates greater instability and consequent sensitivity to hydrodynamic processes operating in the coastal region, with positive or negative variation, especially at Ponta do Tubarão and Barra do Fernandes barrier islands systems, where they are more susceptible to waves impacts, as evidenced in retreat of the shoreline

Investigação das propriedades ópticas de ZnO e ZnO:Al

Pós-graduação em Ciência e Tecnologia de Materiais - FC

Assessment of variance components in nonlinear mixed-effects elliptical models

The issue of assessing variance components is essential in deciding on the inclusion of random effects in the context of mixed models. In this work we discuss this problem by supposing nonlinear elliptical models for correlated data by using the score-type test proposed in Silvapulle and Silvapulle (1995). Being asymptotically equivalent to the likelihood ratio test and only requiring the estimation under the null hypothesis, this test provides a fairly easy computable alternative for assessing one-sided hypotheses in the context of the marginal model. Taking into account the possible non-normal distribution, we assume that the joint distribution of the response variable and the random effects lies in the elliptical class, which includes light-tailed and heavy-tailed distributions such as Student-t, power exponential, logistic, generalized Student-t, generalized logistic, contaminated normal, and the normal itself, among others. We compare the sensitivity of the score-type test under normal, Student-t and power exponential models for the kinetics data set discussed in Vonesh and Carter (1992) and fitted using the model presented in Russo et al. (2009). Also, a simulation study is performed to analyze the consequences of the kurtosis misspecification.

Gauss-Seidel Estimation of Generalized Linear Mixed Models with Application to Poisson Modeling of Spatially Varying Disease Rates

Generalized linear mixed models (GLMMs) provide an elegant framework for the analysis of correlated data. Due to the non-closed form of the likelihood, GLMMs are often fit by computational procedures like penalized quasi-likelihood (PQL). Special cases of these models are generalized linear models (GLMs), which are often fit using algorithms like iterative weighted least squares (IWLS). High computational costs and memory space constraints often make it difficult to apply these iterative procedures to data sets with very large number of cases. This paper proposes a computationally efficient strategy based on the Gauss-Seidel algorithm that iteratively fits sub-models of the GLMM to subsetted versions of the data. Additional gains in efficiency are achieved for Poisson models, commonly used in disease mapping problems, because of their special collapsibility property which allows data reduction through summaries. Convergence of the proposed iterative procedure is guaranteed for canonical link functions. The strategy is applied to investigate the relationship between ischemic heart disease, socioeconomic status and age/gender category in New South Wales, Australia, based on outcome data consisting of approximately 33 million records. A simulation study demonstrates the algorithm's reliability in analyzing a data set with 12 million records for a (non-collapsible) logistic regression model.

Fuentes cartográficas para el estudio de la propiedad de la tierra de la Provincia de Buenos Aires : Chascomús, Ranchos y Monte, 1822-1864

En este trabajo se reconstruye el registro gráfico de 1864 y se comparan los resultados con otros ya desarrollados para 1822, 1830 y 1850, todos en la parte correspondiente a los partidos de Chascomús, Ranchos y Monte. Así, se correlacionan series completas de datos provenientes de duplicados de mensuras, solicitudes de tierras, protocolos de escribanos y testamentarias, con el fin de analizar la extensión de los campos y la tenencia de la tierra en estos partidos. De esta manera se aporta información para utilizar con mejores resultados la cartografía disponible para la época, revelando sus posibilidades expresivas en la comprensión de la ocupación y acceso a la propiedad legal de la tierra durante el siglo XIX, en ese sentido, aquí se evalúa el parcelamiento de los terrenos y el grado de concentración de la propiedad

Fuentes cartográficas para el estudio de la propiedad de la tierra de la Provincia de Buenos Aires : Chascomús, Ranchos y Monte, 1822-1864

En este trabajo se reconstruye el registro gráfico de 1864 y se comparan los resultados con otros ya desarrollados para 1822, 1830 y 1850, todos en la parte correspondiente a los partidos de Chascomús, Ranchos y Monte. Así, se correlacionan series completas de datos provenientes de duplicados de mensuras, solicitudes de tierras, protocolos de escribanos y testamentarias, con el fin de analizar la extensión de los campos y la tenencia de la tierra en estos partidos. De esta manera se aporta información para utilizar con mejores resultados la cartografía disponible para la época, revelando sus posibilidades expresivas en la comprensión de la ocupación y acceso a la propiedad legal de la tierra durante el siglo XIX, en ese sentido, aquí se evalúa el parcelamiento de los terrenos y el grado de concentración de la propiedad

Fuentes cartográficas para el estudio de la propiedad de la tierra de la Provincia de Buenos Aires : Chascomús, Ranchos y Monte, 1822-1864

En este trabajo se reconstruye el registro gráfico de 1864 y se comparan los resultados con otros ya desarrollados para 1822, 1830 y 1850, todos en la parte correspondiente a los partidos de Chascomús, Ranchos y Monte. Así, se correlacionan series completas de datos provenientes de duplicados de mensuras, solicitudes de tierras, protocolos de escribanos y testamentarias, con el fin de analizar la extensión de los campos y la tenencia de la tierra en estos partidos. De esta manera se aporta información para utilizar con mejores resultados la cartografía disponible para la época, revelando sus posibilidades expresivas en la comprensión de la ocupación y acceso a la propiedad legal de la tierra durante el siglo XIX, en ese sentido, aquí se evalúa el parcelamiento de los terrenos y el grado de concentración de la propiedad