Regression with autocorrelated data: A study of time trend of global infant mortality rate

The infant mortality rate (IMR) is considered to be one of the most important indices of a country's well-being. Countries around the world and other health organizations like the World Health Organization are dedicating their resources, knowledge and energy to reduce the infant mortality rates. The well-known Millennium Development Goal 4 (MDG 4), whose aim is to archive a two thirds reduction of the under-five mortality rate between 1990 and 2015, is an example of the commitment. ^ In this study our goal is to model the trends of IMR between the 1950s to 2010s for selected countries. We would like to know how the IMR is changing overtime and how it differs across countries. ^ IMR data collected over time forms a time series. The repeated observations of IMR time series are not statistically independent. So in modeling the trend of IMR, it is necessary to account for these correlations. We proposed to use the generalized least squares method in general linear models setting to deal with the variance-covariance structure in our model. In order to estimate the variance-covariance matrix, we referred to the time-series models, especially the autoregressive and moving average models. Furthermore, we will compared results from general linear model with correlation structure to that from ordinary least squares method without taking into account the correlation structure to check how significantly the estimates change.^

Current meter data from mooring stations in the English Channel (Table 1)

A new technique for the harmonic analysis of current observations is described. It consists in applying a linear band pass filter which separates the various species and removes the contribution of non-tidal effects at intertidal frequencies. The tidal constituents are then evaluated through the method of least squares. In spite of the narrowness of the filter, only three days of data are lost through the filtering procedure and the only requirement on the data is that the time interval between samples be an integer fraction of one day. This technique is illustrated through the analysis of a few French current observations from the English Channel within the framework of INOUT. The characteristics of the main tidal constituents are given.

Iberian Margin 420 kyr geochemical and color variations record

Here we report 420 kyr long records of sediment geochemical and color variations from the southwestern Iberian Margin. We synchronized the Iberian Margin sediment record to Antarctic ice cores and speleothem records on millennial time scales and investigated the phase responses relative to orbital forcing of multiple proxy records available from these cores. Iberian Margin sediments contain strong precession power. Sediment "redness" (a* and 570-560 nm) and the ratio of long-chain alcohols to n-alkanes (C26OH/(C26OH + C29)) are highly coherent and in-phase with precession. Redder layers and more oxidizing conditions (low alcohol ratio) occur near precession minima (summer insolation maxima). We suggest these proxies respond rapidly to low-latitude insolation forcing by wind-driven processes (e.g., dust transport, upwelling, precipitation). Most Iberian Margin sediment parameters lag obliquity maxima by 7-8 ka, indicating a consistent linear response to insolation forcing at obliquity frequencies driven mainly by high-latitude processes. Although the lengths of the time series are short (420 ka) for detecting 100 kyr eccentricity cycles, the phase relationships support those obtained by Shackleton []. Antarctic temperature and the Iberian Margin alcohol ratios (C26OH/(C26OH + C29)) lead eccentricity maxima by 6 kyr, with lower ratios (increased oxygenation) occurring at eccentricity maxima. CO2, CH4, and Iberian SST are nearly in phase with eccentricity, and minimum ice volume (as inferred from Pacific d18Oseawater) lags eccentricity maxima by 10 kyr. The phase relationships derived in this study continue to support a potential role of the Earth's carbon cycle in contributing to the 100 kyr cycle.

Analysis of house prices to assess economic impacts of new public transport infrastructure: Madrid metro line 12.

Transportation infrastructure is known to affect the value of real estate property by virtue of changes in accessibility. The impact of transportation facilities is highly localized as well, and it is possible that spillover effects result from the capitalization of accessibility. The objective of this study was to review the theoretical background related to spatial hedonic models and the opportunities that they provided to evaluate the effect of new transportation infrastructure. An empirical case study is presented: the Madrid Metro Line 12, known as Metrosur, in the region of Madrid, Spain. The effect of proximity to metro stations on housing prices was evaluated. The analysis took into account a host of variables, including structure, location, and neighborhood and made use of three modeling approaches: linear regression estimation with ordinary least squares, spatial error, and spatial lag. The results indicated that better accessibility to Metrosur stations had a positive impact on real estate values and that the effect was marked in cases in which a house was for sale. The results also showed the presence of submarkets, which were well defined by geographic boundaries, and transport fares, which implied that the economic benefits differed across municipalities.

Improved estimation of Weibull Parameters considering unreliability uncertainties

We propose a linear regression method for estimating Weibull parameters from life tests. The method uses stochastic models of the unreliability at each failure instant. As a result, a heteroscedastic regression problem arises that is solved by weighted least squares minimization. The main feature of our method is an innovative s-normalization of the failure data models, to obtain analytic expressions of centers and weights for the regression. The method has been Monte Carlo contrasted with Benard?s approximation, and Maximum Likelihood Estimation; and it has the highest global scores for its robustness, and performance.

Examination of the quality of spinach leaves using hyperspectral imaging

The present research is focused on the application of hyperspectral images for the supervision of quality deterioration in ready to use leafy spinach during storage (Spinacia oleracea). Two sets of samples of packed leafy spinach were considered: (a) a first set of samples was stored at 20 °C (E-20) in order to accelerate the degradation process, and these samples were measured the day of reception in the laboratory and after 2 days of storage; (b) a second set of samples was kept at 10 °C (E-10), and the measurements were taken throughout storage, beginning the day of reception and repeating the acquisition of Images 3, 6 and 9 days later. Twenty leaves per test were analyzed. Hyperspectral images were acquired with a push-broom CCD camera equipped with a spectrograph VNIR (400–1000 nm). Calibration set of spectra was extracted from E-20 samples, containing three classes of degradation: class A (optimal quality), class B and class C (maximum deterioration). Reference average spectra were defined for each class. Three models, computed on the calibration set, with a decreasing degree of complexity were compared, according to their ability for segregating leaves at different quality stages (fresh, with incipient and non-visible symptoms of degradation, and degraded): spectral angle mapper distance (SAM), partial least squares discriminant analysis models (PLS-DA), and a non linear index (Leafy Vegetable Evolution, LEVE) combining five wavelengths were included among the previously selected by CovSel procedure. In sets E-10 and E-20, artificial images of the membership degree according to the distance of each pixel to the reference classes, were computed assigning each pixel to the closest reference class. The three methods were able to show the degradation of the leaves with storage time.

Robust regression applied to fractal/multifractal analysis.

Fractal and multifractal are concepts that have grown increasingly popular in recent years in the soil analysis, along with the development of fractal models. One of the common steps is to calculate the slope of a linear fit commonly using least squares method. This shouldn?t be a special problem, however, in many situations using experimental data the researcher has to select the range of scales at which is going to work neglecting the rest of points to achieve the best linearity that in this type of analysis is necessary. Robust regression is a form of regression analysis designed to circumvent some limitations of traditional parametric and non-parametric methods. In this method we don?t have to assume that the outlier point is simply an extreme observation drawn from the tail of a normal distribution not compromising the validity of the regression results. In this work we have evaluated the capacity of robust regression to select the points in the experimental data used trying to avoid subjective choices. Based on this analysis we have developed a new work methodology that implies two basic steps: ? Evaluation of the improvement of linear fitting when consecutive points are eliminated based on R pvalue. In this way we consider the implications of reducing the number of points. ? Evaluation of the significance of slope difference between fitting with the two extremes points and fitted with the available points. We compare the results applying this methodology and the common used least squares one. The data selected for these comparisons are coming from experimental soil roughness transect and simulated based on middle point displacement method adding tendencies and noise. The results are discussed indicating the advantages and disadvantages of each methodology.

Automatic detection of crop rows in maize fields with high weeds pressure

This paper proposes a new method, oriented to crop row detection in images from maize fields with high weed pressure. The vision system is designed to be installed onboard a mobile agricultural vehicle, i.e. submitted to gyros, vibrations and undesired movements. The images are captured under image perspective, being affected by the above undesired effects. The image processing consists of three main processes: image segmentation, double thresholding, based on the Otsu’s method, and crop row detection. Image segmentation is based on the application of a vegetation index, the double thresholding achieves the separation between weeds and crops and the crop row detection applies least squares linear regression for line adjustment. Crop and weed separation becomes effective and the crop row detection can be favorably compared against the classical approach based on the Hough transform. Both gain effectiveness and accuracy thanks to the double thresholding that makes the main finding of the paper.

Análisis, desarrollo y optimización de un sistema para el diseño de redes topográficas valorando el modelo digital del terreno

El presente trabajo tiene como objetivo general el análisis de las técnicas de diseño y optimización de redes topográficas, observadas mediante topografía convencional (no satelital) el desarrollo e implementación de un sistema informático capaz de ayudar a la definición de la geometría más fiable y precisa, en función de la orografía del terreno donde se tenga que ubicar. En primer lugar se realizará un estudio de la metodología del ajuste mediante mínimos cuadrados y la propagación de varianzas, para posteriormente analizar su dependencia de la geometría que adopte la red. Será imprescindible determinar la independencia de la matriz de redundancia (R) de las observaciones y su total dependencia de la geometría, así como la influencia de su diagonal principal (rii), números de redundancia, para garantizar la máxima fiabilidad interna de la misma. También se analizará el comportamiento de los números de redundancia (rii) en el diseño de una red topográfica, la variación de dichos valores en función de la geometría, analizando su independencia respecto de las observaciones así como los diferentes niveles de diseño en función de los parámetros y datos conocidos. Ha de señalarse que la optimización de la red, con arreglo a los criterios expuestos, está sujeta a los condicionantes que impone la necesidad de que los vértices sean accesibles, y además sean visibles entre sí, aquellos relacionados por observaciones, situaciones que dependen esencialmente del relieve del terreno y de los obstáculos naturales o artificiales que puedan existir. Esto implica la necesidad de incluir en el análisis y en el diseño, cuando menos de un modelo digital del terreno (MDT), aunque lo más útil sería la inclusión en el estudio del modelo digital de superficie (MDS), pero esta opción no siempre será posible. Aunque el tratamiento del diseño esté basado en un sistema bidimensional se estudiará la posibilidad de incorporar un modelo digital de superficie (MDS); esto permitirá a la hora de diseñar el emplazamiento de los vértices de la red la viabilidad de las observaciones en función de la orografía y los elementos, tanto naturales como artificiales, que sobre ella estén ubicados. Este sistema proporcionaría, en un principio, un diseño óptimo de una red constreñida, atendiendo a la fiabilidad interna y a la precisión final de sus vértices, teniendo en cuenta la orografía, lo que equivaldría a resolver un planteamiento de diseño en dos dimensiones y media1; siempre y cuando se dispusiera de un modelo digital de superficie o del terreno. Dado que la disponibilidad de obtener de manera libre el MDS de las zonas de interés del proyecto, hoy en día es costoso2, se planteará la posibilidad de conjuntar, para el estudio del diseño de la red, de un modelo digital del terreno. Las actividades a desarrollar en el trabajo de esta tesis se describen en esta memoria y se enmarcan dentro de la investigación para la que se plantean los siguientes objetivos globales: 1. Establecer un modelo matemático del proceso de observación de una red topográfica, atendiendo a todos los factores que intervienen en el mismo y a su influencia sobre las estimaciones de las incógnitas que se obtienen como resultado del ajuste de las observaciones. 2. Desarrollar un sistema que permita optimizar una red topográfica en sus resultados, aplicando técnicas de diseño y simulación sobre el modelo anterior. 3. Presentar una formulación explícita y rigurosa de los parámetros que valoran la fiabilidad de una red topográfica y de sus relaciones con el diseño de la misma. El logro de este objetivo se basa, además de en la búsqueda y revisión de las fuentes, en una intensa labor de unificación de notaciones y de construcción de pasos intermedios en los desarrollos matemáticos. 4. Elaborar una visión conjunta de la influencia del diseño de una red, en los seis siguientes factores (precisiones a posteriori, fiabilidad de las observaciones, naturaleza y viabilidad de las mismas, instrumental y metodología de estacionamiento) como criterios de optimización, con la finalidad de enmarcar el tema concreto que aquí se aborda. 5. Elaborar y programar los algoritmos necesarios para poder desarrollar una aplicación que sea capaz de contemplar las variables planteadas en el apartado anterior en el problema del diseño y simulación de redes topográficas, contemplando el modelo digital de superficie. Podrían considerarse como objetivos secundarios, los siguientes apartados: Desarrollar los algoritmos necesarios para interrelacionar el modelo digital del terreno con los propios del diseño. Implementar en la aplicación informática la posibilidad de variación, por parte del usuario, de los criterios de cobertura de los parámetros (distribución normal o t de Student), así como los grados de fiabilidad de los mismos ABSTRACT The overall purpose of this work is the analysis of the techniques of design and optimization for geodetic networks, measured with conventional survey methods (not satellite), the development and implementation of a computational system capable to help on the definition of the most liable and accurate geometry, depending on the land orography where the network has to be located. First of all, a study of the methodology by least squares adjustment and propagation of variances will be held; then, subsequently, analyze its dependency of the geometry that the network will take. It will be essential to determine the independency of redundancy matrix (R) from the observations and its absolute dependency from the network geometry, as well as the influence of the diagonal terms of the R matrix (rii), redundancy numbers, in order to ensure maximum re liability of the network. It will also be analyzed first the behavior of redundancy numbers (rii) in surveying network design, then the variation of these values depending on the geometry with the analysis of its independency from the observations, and finally the different design levels depending on parameters and known data. It should be stated that network optimization, according to exposed criteria, is subject to the accessibility of the network points. In addition, common visibility among network points, which of them are connected with observations, has to be considered. All these situations depends essentially on the terrain relief and the natural or artificial obstacles that should exist. Therefore, it is necessary to include, at least, a digital terrain model (DTM), and better a digital surface model (DSM), not always available. Although design treatment is based on a bidimensional system, the possibility of incorporating a digital surface model (DSM) will be studied; this will allow evaluating the observations feasibility based on the terrain and the elements, both natural and artificial, which are located on it, when selecting network point locations. This system would provide, at first, an optimal design of a constrained network, considering both the internal reliability and the accuracy of its points (including the relief). This approach would amount to solving a “two and a half dimensional”3 design, if a digital surface model is available. As the availability of free DSM4 of the areas of interest of the project today is expensive, the possibility of combining a digital terrain model will arise. The activities to be developed on this PhD thesis are described in this document and are part of the research for which the following overall objectives are posed: 1. To establish a mathematical model for the process of observation of a survey network, considering all the factors involved and its influence on the estimates of the unknowns that are obtained as a result of the observations adjustment. 2. To develop a system to optimize a survey network results, applying design and simulation techniques on the previous model. 3. To present an explicit and rigorous formulation of parameters which assess the reliability of a survey network and its relations with the design. The achievement of this objective is based, besides on the search and review of sources, in an intense work of unification of notation and construction of intermediate steps in the mathematical developments. 4. To develop an overview of the influence on the network design of six major factors (posterior accuracy, observations reliability, viability of observations, instruments and station methodology) as optimization criteria, in order to define the subject approached on this document. 5. To elaborate and program the algorithms needed to develop an application software capable of considering the variables proposed in the previous section, on the problem of design and simulation of surveying networks, considering the digital surface model. It could be considered as secondary objectives, the following paragraphs: To develop the necessary algorithms to interrelate the digital terrain model with the design ones. To implement in the software application the possibility of variation of the coverage criteria parameters (normal distribution or Student t test) and therefore its degree of reliability.

QSAR e dinâmica molecular no estudo de sistemas biomoleculares: predição da atividade biológica de antagonistas do receptor sigma-1 e simulações de bicamadas lipídicas

Diferentes abordagens teóricas têm sido utilizadas em estudos de sistemas biomoleculares com o objetivo de contribuir com o tratamento de diversas doenças. Para a dor neuropática, por exemplo, o estudo de compostos que interagem com o receptor sigma-1 (Sig-1R) pode elucidar os principais fatores associados à atividade biológica dos mesmos. Nesse propósito, estudos de Relações Quantitativas Estrutura-Atividade (QSAR) utilizando os métodos de regressão por Mínimos Quadrados Parciais (PLS) e Rede Neural Artificial (ANN) foram aplicados a 64 antagonistas do Sig-1R pertencentes à classe de 1-arilpirazóis. Modelos PLS e ANN foram utilizados com o objetivo de descrever comportamentos lineares e não lineares, respectivamente, entre um conjunto de descritores e a atividade biológica dos compostos selecionados. O modelo PLS foi obtido com 51 compostos no conjunto treinamento e 13 compostos no conjunto teste (r² = 0,768, q² = 0,684 e r²teste = 0,785). Testes de leave-N-out, randomização da atividade biológica e detecção de outliers confirmaram a robustez e estabilidade dos modelos e mostraram que os mesmos não foram obtidos por correlações ao acaso. Modelos também foram gerados a partir da Rede Neural Artificial Perceptron de Multicamadas (MLP-ANN), sendo que a arquitetura 6-12-1, treinada com as funções de transferência tansig-tansig, apresentou a melhor resposta para a predição da atividade biológica dos compostos (r²treinamento = 0,891, r²validação = 0,852 e r²teste = 0,793). Outra abordagem foi utilizada para simular o ambiente de membranas sinápticas utilizando bicamadas lipídicas compostas por POPC, DOPE, POPS e colesterol. Os estudos de dinâmica molecular desenvolvidos mostraram que altas concentrações de colesterol induzem redução da área por lipídeo e difusão lateral e aumento na espessura da membrana e nos valores de parâmetro de ordem causados pelo ordenamento das cadeias acil dos fosfolipídeos. As bicamadas lipídicas obtidas podem ser usadas para simular interações entre lipídeos e pequenas moléculas ou proteínas contribuindo para as pesquisas associadas a doenças como Alzheimer e Parkinson. As abordagens usadas nessa tese são essenciais para o desenvolvimento de novas pesquisas em Química Medicinal Computacional.

Relação espacial dos óbitos e internações por tuberculose com indicadores sociais em Ribeirão Preto (SP)

Objetivo: Propôs-se analisar a relação espacial dos óbitos e internações evitáveis por TB com indicadores sociais em Ribeirão Preto/SP. Métodos: Trata-se de um estudo ecológico em que foram considerados os casos de óbitos e internações, tendo como causa básica do óbito e motivo principal da internação, a tuberculose (CID A15.0 a A19.9), ocorridos na zona urbana de Ribeirão Preto e registrados respectivamente no Sistema de Informação sobre Mortalidade e no Sistema de Internação Hospitalar do Sistema Único de Saúde no período de 2006 a 2012. Foi realizada a análise univariada das variáveis sociodemográficas e operacionais dos casos investigados. Para construção dos indicadores sociais utilizou-se a análise de componentes principais, sendo selecionados dados das áreas de abrangência do município, considerando os dados do Censo Demográfico de 2010. A geocodificação dos casos foi processada no TerraView versão 4.2.2. Recorreu-se à regressão linear múltipla, pelo método dos mínimos quadrados e à regressão espacial para análise da relação de dependência espacial entre os indicadores sociais e as taxas de mortalidade e de internações por TB. A autocorrelação nos resíduos da regressão linear múltipla foi testada por meio do Teste Global de Moran, as análises foram realizadas considerando os softwares Arcgis-versão 10.1, Statistica versão 12.0, OpenGeoDa versão 1.0 e R versão 3.2.3. Para o diagnóstico do melhor modelo de regressão espacial, utilizou-se o teste Multiplicador de Lagrange. Em todos os testes, foi fixado o nivel de significancia de alfa em 5% (p< 0,05). Resultados: Foram registrados 50 casos de óbitos e 196 casos de internações por TB. A maioria dos casos registrados em ambos os sistemas se deu em pessoas do sexo masculino (n=41; 82%/n=146; 74,5%) e com a forma clínica pulmonar (n=44; 80,0%/n=138; 67,9%). Na construção dos indicadores sociais, três novas variáveis surgiram, apresentando respectivamente variância total de 46,2%, 18,7% e 14,6% sendo denominadas como indicadores de renda, desigualdade social e equidade social. Na modelagem para verificar relação espacial entre os óbitos e os indicadores sociais observou-se que a equidade social foi indicador estatisticamente significativo (p=0,0013) com relação negativa a mortalidade, sendo o Modelo da Defasagem Espacial o melhor método para testar a dependência espacial, com valor de ? (rho) estimado em 0,53 e altamente significativo (p=0,0014). Já na modelagem da relação espacial entre as internações por tuberculose e os indicadores sociais, o indicador de renda apresentou-se estatisticamente significativo (p=0,015) com relação negativa a internação e o melhor método para testar a dependência espacial também foi o Modelo da Defasagem Espacial com valor de ? (rho) estimado em 0,80 e altamente significativo (p<0,0001). Conclusão: O estudo contribuiu no avanço do conhecimento de que a mortalidade e as internações por tuberculose são eventos socialmente determinados, o que sugere investimento por parte da gestão

Validation of the Item-Attribute Matrix in TIMSS-Mathematics Using Multiple Regression and the LSDM

The purposes of this study were (1) to validate of the item-attribute matrix using two levels of attributes (Level 1 attributes and Level 2 sub-attributes), and (2) through retrofitting the diagnostic models to the mathematics test of the Trends in International Mathematics and Science Study (TIMSS), to evaluate the construct validity of TIMSS mathematics assessment by comparing the results of two assessment booklets. Item data were extracted from Booklets 2 and 3 for the 8th grade in TIMSS 2007, which included a total of 49 mathematics items and every student's response to every item. The study developed three categories of attributes at two levels: content, cognitive process (TIMSS or new), and comprehensive cognitive process (or IT) based on the TIMSS assessment framework, cognitive procedures, and item type. At level one, there were 4 content attributes (number, algebra, geometry, and data and chance), 3 TIMSS process attributes (knowing, applying, and reasoning), and 4 new process attributes (identifying, computing, judging, and reasoning). At level two, the level 1 attributes were further divided into 32 sub-attributes. There was only one level of IT attributes (multiple steps/responses, complexity, and constructed-response). Twelve Q-matrices (4 originally specified, 4 random, and 4 revised) were investigated with eleven Q-matrix models (QM1 ~ QM11) using multiple regression and the least squares distance method (LSDM). Comprehensive analyses indicated that the proposed Q-matrices explained most of the variance in item difficulty (i.e., 64% to 81%). The cognitive process attributes contributed to the item difficulties more than the content attributes, and the IT attributes contributed much more than both the content and process attributes. The new retrofitted process attributes explained the items better than the TIMSS process attributes. Results generated from the level 1 attributes and the level 2 attributes were consistent. Most attributes could be used to recover students' performance, but some attributes' probabilities showed unreasonable patterns. The analysis approaches could not demonstrate if the same construct validity was supported across booklets. The proposed attributes and Q-matrices explained the items of Booklet 2 better than the items of Booklet 3. The specified Q-matrices explained the items better than the random Q-matrices.

On increasing the effective aperture of antennas by data processing /

Includes bibliographical references (p. 58-59)

The use of correlation and regression methods in optometry

Correlation and regression are two of the statistical procedures most widely used by optometrists. However, these tests are often misused or interpreted incorrectly, leading to erroneous conclusions from clinical experiments. This review examines the major statistical tests concerned with correlation and regression that are most likely to arise in clinical investigations in optometry. First, the use, interpretation and limitations of Pearson's product moment correlation coefficient are described. Second, the least squares method of fitting a linear regression to data and for testing how well a regression line fits the data are described. Third, the problems of using linear regression methods in observational studies, if there are errors associated in measuring the independent variable and for predicting a new value of Y for a given X, are discussed. Finally, methods for testing whether a non-linear relationship provides a better fit to the data and for comparing two or more regression lines are considered.

Does money matter in inflation forecasting

This paper provides the most fully comprehensive evidence to date on whether or not monetary aggregates are valuable for forecasting US inflation in the early to mid 2000s. We explore a wide range of different definitions of money, including different methods of aggregation and different collections of included monetary assets. In our forecasting experiment we use two non-linear techniques, namely, recurrent neural networks and kernel recursive least squares regression - techniques that are new to macroeconomics. Recurrent neural networks operate with potentially unbounded input memory, while the kernel regression technique is a finite memory predictor. The two methodologies compete to find the best fitting US inflation forecasting models and are then compared to forecasts from a naive random walk model. The best models were non-linear autoregressive models based on kernel methods. Our findings do not provide much support for the usefulness of monetary aggregates in forecasting inflation.