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Homicide and public security indicator trends in the city of Sao Paulo between 1996 and 2008: a time-series ecological study

Tourinho Peres, Maria Fernanda; de Almeida, Juliana Feliciano; Vicentin, Diego; Ruotti, Caren; Nery, Marcelo Batista; Cerda, Magdalena; Cardia, Nancy; Adorno, Sergio
Fonte: ABRASCO; RIO DE JANEIRO Publicador: ABRASCO; RIO DE JANEIRO
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
65.82%
The scope of this paper was to analyze the association between homicides and public security indicators in Sao Paulo between 1996 and 2008, after monitoring the unemployment rate and the proportion of youths in the population. A time-series ecological study for 1996 and 2008 was conducted with Sao Paulo as the unit of analysis. Dependent variable: number of deaths by homicide per year. Main independent variables: arrest-incarceration rate, access to firearms, police activity. Data analysis was conducted using Stata. IC 10.0 software. Simple and multivariate negative binomial regression models were created. Deaths by homicide and arrest-incarceration, as well as police activity were significantly associated in simple regression analysis. Access to firearms was not significantly associated to the reduction in the number of deaths by homicide (p>0,05). After adjustment, the associations with both the public security indicators were not significant. In Sao Paulo the role of public security indicators are less important as explanatory factors for a reduction in homicide rates, after adjustment for unemployment rate and a reduction in the proportion of youths. The results reinforce the importance of socioeconomic and demographic factors for a change in the public security scenario in Sao Paulo.

Mortalidade por homicídios, acidentes de transporte e suicídios no município de Belo Horizonte e região metropolitana, em série histórica de 1980-2000; Mortality from Homicides, Traffic Accidents and Suicides in Belo Horizonte and the Metropolitan region, in a historical time series from 1980 - 2000

Villela, Lenice de Castro Mendes
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Tese de Doutorado Formato: application/pdf
Publicado em 16/02/2005 Português
Relevância na Pesquisa
65.82%
Objetivo: Estudar o perfil epidemiológico da mortalidade por Homicídios, Acidentes de Transporte e Suicídios no município de Belo Horizonte e Região Metropolitana, na série histórica de 1980 a 2000. Métodos: O estudo apresenta um desenho ecológico, do tipo série histórica. Os indicadores de mortalidade foram os coeficientes específicos por sexo, idade e gerais padronizados; a mortalidade proporcional; a razão de mortalidade segundo sexo e idade e os incrementos / decrementos percentuais. A população utilizada como padrão foi a de 1980. Os óbitos por Homicídios, Acidentes de Transporte e Suicídios e as estimativas populacionais, segundo o ano calendário, sexo, idade e município de residência foram extraídos da base de dados do DATASUS. No período entre 1980 e 1995, os óbitos foram codificados, segundo a IX Classificação Internacional de Doenças - CID 9ª Revisão, e, a partir de 1996, segundo a CID - 10ª Revisão. A análise de tendência temporal foi desenvolvida no software SPSS para Windows, utilizando-se a técnica de regressão linear simples, com nível de significância (? < 0,05). Resultados: Nas duas regiões geográficas, os indicadores de mortalidade apresentaram maior magnitude para o sexo masculino. A razão de coeficientes específicos de mortalidade apresentou maior magnitude nas faixas etárias entre 20 e 49 anos. Os coeficientes específicos de mortalidade por Homicídios apresentaram maior magnitude na região Metropolitana e os Suicídios e Acidentes de Transporte...

The impact of OFDI on economic growth countries. An econometric approach using panel data and time-series evidence

Ambrosini, Mattia
Fonte: Fundação Getúlio Vargas Publicador: Fundação Getúlio Vargas
Tipo: Dissertação
Português
Relevância na Pesquisa
65.84%
The thesis at hand adds to the existing literature by investigating the relationship between economic growth and outward foreign direct investments (OFDI) on a set of 16 emerging countries. Two different econometric techniques are employed: a panel data regression analysis and a time-series causality analysis. Results from the regression analysis indicate a positive and significant correlation between OFDI and economic growth. Additionally, the coefficient for the OFDI variable is robust in the sense specified by the Extreme Bound Analysis (EBA). On the other hand, the findings of the causality analysis are particularly heterogeneous. The vector autoregression (VAR) and the vector error correction model (VECM) approaches identify unidirectional Granger causality running either from OFDI to GDP or from GDP to OFDI in six countries. In four economies causality among the two variables is bidirectional, whereas in five countries no causality relationship between OFDI and GDP seems to be present.

Programação genética para predição de séries temporais aplicados a mercados financeiros; Genetic programming for time series forecasting applied to financial markets

Prochnow, Fabio Alberto
Fonte: Universidade Federal do Rio Grande do Sul Publicador: Universidade Federal do Rio Grande do Sul
Tipo: Trabalho de Conclusão de Curso Formato: application/pdf
Português
Relevância na Pesquisa
65.99%
As Séries Temporais podem ser percebidas em diversas formas na natureza e até mesmo nos processos industriais. Nos Mercados Financeiros, por exemplo, pode-se ver nitidamente a formação destas séries. Tanto para os investidores do Mercado Forex quando para os do Mercado de Ações, o desafio é prever as variações destas séries e obter o maior lucro possível destes comportamentos. Para isso, foi criada a Análise Técnica, que consiste de fundamentos e ferramentas de análise gráfica para auxiliar os investidores na hora de tomar uma decisão. Ao encontro disso, surgem os métodos clássicos de predição de Séries Temporais como o Naïve, o ARIMA e, nos últimos tempos, as próprias Redes Neurais. Por outro lado, a Programação Genética vem se destacando em inúmeras aplicações práticas e, dentre as possibilidades de uso desta, está a Regressão Simbólica. Por esse motivo, realizaram-se experimentos comparativos entre os métodos mais utilizados para a previsão destas séries e a própria predição por Regressão Simbólica. Para isso, foram coletadas séries referentes aos artigos mais movimentados nos Mercados de Ações e Forex como as ações PETR4 e VALE5 e os pares EURUSD e GBPUSD. Por fim, percebe-se que a Regressão Simbólica pode ser mais um aliado dos investidores na busca pelo lucro e...

Evolutionary optimization of sparsely connected and time-lagged neural networks for time series forecasting

Peralta Donate, Juan; Cortez, Paulo
Fonte: Elsevier Publicador: Elsevier
Tipo: Artigo de Revista Científica
Publicado em //2014 Português
Relevância na Pesquisa
75.79%
Time Series Forecasting (TSF) is an important tool to support decision mak- ing (e.g., planning production resources). Artificial Neural Networks (ANN) are innate candidates for TSF due to advantages such as nonlinear learn- ing and noise tolerance. However, the search for the best model is a complex task that highly affects the forecasting performance. In this work, we propose two novel Evolutionary Artificial Neural Networks (EANN) approaches for TSF based on an Estimation Distribution Algorithm (EDA) search engine. The first new approach consist of Sparsely connected Evolutionary ANN (SEANN), which evolves more flexible ANN structures to perform multi-step ahead forecasts. The second one, consists of an automatic Time lag feature selection EANN (TEANN) approach that evolves not only ANN parameters (e.g., input and hidden nodes, training parameters) but also which set of time lags are fed into the forecasting model. Several experiments were held, using a set of six time series, from different real-world domains. Also, two error metrics (i.e., Mean Squared Error and Symmetric Mean Absolute Per- centage Error) were analyzed. The two EANN approaches were compared against a base EANN (with no ANN structure or time lag optimization) and four other methods (Autoregressive Integrated Moving Average method...

Time series analysis of water surface temperature and heat flux components in the Itumbiara Reservoir (GO), Brazil

Alcântara,Enner Herenio de; Stech,José Luiz; Lorenzzetti,João Antônio; Novo,Evlyn Márcia Leão de Moraes
Fonte: Associação Brasileira de Limnologia Publicador: Associação Brasileira de Limnologia
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/09/2011 Português
Relevância na Pesquisa
65.84%
AIM: Water temperature plays an important role in ecological functioning and in controlling the biogeochemical processes of the aquatic system. Conventional water quality monitoring is expensive and time consuming. It is particularly challenging for large water bodies. Conversely, remote sensing can be considered a powerful tool to assess important properties of aquatic systems because it provides synoptic and frequent data acquisition over large areas. The objective of this study was to analyze time series of surface water temperature and heat flux to advance the understanding of temporal variations in a hydroelectric reservoir. METHOD: MODIS water-surface temperature (WST) level 2, 1 km nominal resolution data (MOD11L2, version 5) were used. All available clear-sky MODIS/Terra images from 2003 to 2008 were used, resulting in a total of 786 daytime and 473 nighttime images. Time series of surface water temperature was obtained computing the monthly mean in a 3×3 window of three reservoir selected sites: 1) near the dam, 2) at the centre of the reservoir and 3) in the confluence of the rivers. In-situ meteorological data from 2003 to 2008 were used to calculate surface energy budget time series. Cross-wavelet, coherence and phase analysis were carried out to compute the correlation between daytime and nighttime surface water temperatures and the computed heat fluxes. RESULTS: The monthly mean of the day-time WST shows lager variability than the night-time WST. All time series (daytime and nighttime) have a cyclical pattern...

Time series regression studies in environmental epidemiology

Bhaskaran, Krishnan; Gasparrini, Antonio; Hajat, Shakoor; Smeeth, Liam; Armstrong, Ben
Fonte: Oxford University Press Publicador: Oxford University Press
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
65.95%
Time series regression studies have been widely used in environmental epidemiology, notably in investigating the short-term associations between exposures such as air pollution, weather variables or pollen, and health outcomes such as mortality, myocardial infarction or disease-specific hospital admissions. Typically, for both exposure and outcome, data are available at regular time intervals (e.g. daily pollution levels and daily mortality counts) and the aim is to explore short-term associations between them. In this article, we describe the general features of time series data, and we outline the analysis process, beginning with descriptive analysis, then focusing on issues in time series regression that differ from other regression methods: modelling short-term fluctuations in the presence of seasonal and long-term patterns, dealing with time varying confounding factors and modelling delayed (‘lagged’) associations between exposure and outcome. We finish with advice on model checking and sensitivity analysis, and some common extensions to the basic model.

A Systematic Review of Methodology: Time Series Regression Analysis for Environmental Factors and Infectious Diseases

Imai, Chisato; Hashizume, Masahiro
Fonte: The Japanese Society of Tropical Medicine Publicador: The Japanese Society of Tropical Medicine
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
65.86%
Background: Time series analysis is suitable for investigations of relatively direct and short-term effects of exposures on outcomes. In environmental epidemiology studies, this method has been one of the standard approaches to assess impacts of environmental factors on acute non-infectious diseases (e.g. cardiovascular deaths), with conventionally generalized linear or additive models (GLM and GAM). However, the same analysis practices are often observed with infectious diseases despite of the substantial differences from non-infectious diseases that may result in analytical challenges. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, systematic review was conducted to elucidate important issues in assessing the associations between environmental factors and infectious diseases using time series analysis with GLM and GAM. Published studies on the associations between weather factors and malaria, cholera, dengue, and influenza were targeted. Findings: Our review raised issues regarding the estimation of susceptible population and exposure lag times, the adequacy of seasonal adjustments, the presence of strong autocorrelations, and the lack of a smaller observation time unit of outcomes (i.e. daily data). These concerns may be attributable to features specific to infectious diseases...

Semiparametric Efficient Estimation in Time Series

Hodgson, Douglas J.
Fonte: University of Rochester. Rochester Center for Economic Research. Publicador: University of Rochester. Rochester Center for Economic Research.
Tipo: Trabalho em Andamento
Português
Relevância na Pesquisa
75.85%
We obtain semiparametric efficiency bounds for estimation of a location parameter in a time series model where the innovations are stationary and ergodic conditionally symmetric martingale differences but otherwise possess general dependence and distributions of unknown form. We then describe an iterative estimator that achieves this bound when the conditional density functions of the sample are known. Finally, we develop a "semi-adaptive" estimator that achieves the bound when these densities are unknown by the investigator. This estimator employs nonparametric kernel estimates of the densities. We show that this estimator has robustness properties in the presence of a certain degree of nonstationarity. We extend the method to the estimation of time series regression models and report some Monte Carlo results.

Tests of Joint Hypotheses for Time Series Regression with a Unit Root

PERRON, Pierre
Fonte: Université de Montréal Publicador: Université de Montréal
Tipo: Artigo de Revista Científica Formato: 657539 bytes; application/pdf
Português
Relevância na Pesquisa
85.89%
This Paper Studies Tests of Joint Hypotheses in Time Series Regression with a Unit Root in Which Weakly Dependent and Heterogeneously Distributed Innovations Are Allowed. We Consider Two Types of Regression: One with a Constant and Lagged Dependent Variable, and the Other with a Trend Added. the Statistics Studied Are the Regression "F-Test" Originally Analysed by Dickey and Fuller (1981) in a Less General Framework. the Limiting Distributions Are Found Using Functinal Central Limit Theory. New Test Statistics Are Proposed Which Require Only Already Tabulated Critical Values But Which Are Valid in a Quite General Framework (Including Finite Order Arma Models Generated by Gaussian Errors). This Study Extends the Results on Single Coefficients Derived in Phillips (1986A) and Phillips and Perron (1986).

Dividend persistence and return predictability

Powell, John G; Shi, Jing; Smith, Tom
Fonte: Universidade Nacional da Austrália Publicador: Universidade Nacional da Austrália
Tipo: Working/Technical Paper Formato: 274845 bytes; 350 bytes; application/pdf; application/octet-stream
Português
Relevância na Pesquisa
65.94%
Evidence of dividend yield return predictability has been presented so widely and consistently that the result has tended to be generally accepted. This paper shows that return predictability of the dividend yield is a spurious result that is due to dividend persistence and finds that standard dividend behaviour explanatory models are also affected by the spurious regression problem. A simulation procedure is utilized to take account of a spurious correlation that compounds the spurious regression problem when the dependent and independent variables in a time series regression are ratios composed of common component variables. The paper’s results therefore imply that extreme care should be taken when using ratios as predictor or explanatory variables in time series regression. The paper introduces a reformulated Lintner first difference dividend behaviour model that is not subject to spurious regression.; no

Statistical inference of nonlinear Granger causality: a semiparametric time series regression analysis.

Lee, Sooyoung
Fonte: Universidade de Adelaide Publicador: Universidade de Adelaide
Tipo: Tese de Doutorado
Publicado em //2013 Português
Relevância na Pesquisa
96.02%
Since the seminal work of Granger (1969), Granger causality has become a useful concept and tool in the study of the dynamic linkages between economic variables and to explore whether or not an economic variable helps forecast another one. Researchers have suggested a variety of methods to test the existence of Grangercausality in the literature. In particular, linear Granger causality testing has been remarkably developed; (see, for example, Toda & Philips (1993), Sims, Stock & Watson (1990), Geweke (1982), Hosoya (1991) and Hidalgo (2000)). However, in practice, the real economic relationship between different variables may often be nonlinear. Hiemstra & Jones (1994) and Nishiyama, Hitomi, Kawasaki & Jeong (2011) recently proposed different methods to test the existence of any non-linear Granger causality between a pair of economic variables under a α-mixing framework of data generating process. Their methods are general with nonparametric features, which however suffer from curse of dimensionality when high lag orders need to be taken into consideration in applications. In this thesis, the main objective is to develop a class of semiparametric time series regression models that are of partially linear structures, with statistical theory established under a more general framework of near epoch dependent (NED) data generating processes...

Page's Sequential Procedure for Change-Point Detection in Time Series Regression

Fremdt, Stefan
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 06/08/2013 Português
Relevância na Pesquisa
65.84%
In a variety of different settings cumulative sum (CUSUM) procedures have been applied for the sequential detection of structural breaks in the parameters of stochastic models. Yet their performance depends strongly on the time of change and is best under early-change scenarios. For later changes their finite sample behavior is rather questionable. We therefore propose modified CUSUM procedures for the detection of abrupt changes in the regression parameter of multiple time series regression models, that show a higher stability with respect to the time of change than ordinary CUSUM procedures. The asymptotic distributions of the test statistics and the consistency of the procedures are provided. In a simulation study it is shown that the proposed procedures behave well in finite samples. Finally the procedures are applied to a set of capital asset pricing data related to the Fama-French extension of the capital asset pricing model.

Directed Time Series Regression for Control

Kao, Yi-Hao; Van Roy, Benjamin
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 26/06/2012 Português
Relevância na Pesquisa
65.93%
We propose directed time series regression, a new approach to estimating parameters of time-series models for use in certainty equivalent model predictive control. The approach combines merits of least squares regression and empirical optimization. Through a computational study involving a stochastic version of a well known inverted pendulum balancing problem, we demonstrate that directed time series regression can generate significant improvements in controller performance over either of the aforementioned alternatives.

A Time Series Regression Analysis of Future Climate

Rudulph, Jake
Fonte: Universidade Duke Publicador: Universidade Duke
Tipo: Masters' project
Publicado em 23/04/2012 Português
Relevância na Pesquisa
65.86%
Current approaches to climate modeling, including environmental simulation, may not be able to generate actionable results for a few decades yet. Over the last 50 years, methods attempting to capture and predict states of the climate system have flourished and diversified. However, many such models are subject to errors and uncertainty arising from parameterization problems, the obligate characterization of poorly understood phenomena, and high capacity requirements stemming from the incredible computing power needed. As the window for meaningful actions towards altering the climate change trajectory closes, we should consider the use of simple methods that generally predict the conditions of the future climate. For my analysis, I developed a time-series regression analysis of land surface trends in precipitation and near-surface temperature. For each global 0.5º land surface grid, values for 1901-2009 baseline means were calculated, and 2050 values were predicted using time series regression models for each of four historical data subsets. Average predicted warming across the subsets range from 0.89 ºC to 5.8 ºC above the baseline, with high northern latitudes predicted to experience the most warming. Precipitation is predicted to follow the “wet getting wetter...

Performance Evaluation of Zanzibar's Malaria Case Notification (MCN) System: The Assessment of Timeliness and Stakeholder Interaction

Khandekar, Eeshan
Fonte: Universidade Duke Publicador: Universidade Duke
Tipo: Tese de Doutorado
Publicado em //2015 Português
Relevância na Pesquisa
65.87%

Malaria places a tremendous burden on the world's developing countries, with latest estimates making malaria responsible for 198 million cases and 584,000 deaths in 2013. Recent success in malaria control reducing prevalence across the world, however, has placed the goal of malaria elimination at the forefront of countries' malaria strategies. Malaria elimination is the reduction of locally acquired malaria prevalence to zero. Due to the risk each malaria case poses for onward transmission of malaria, quickly detecting and treating all cases of malaria is crucial for malaria elimination. As a result, a robust surveillance system that can track all cases in real-time should be at the core of any malaria elimination program.

One region embarking on malaria elimination is Zanzibar, a semi-autonomous region of Tanzania. Zanzibar has instituted a malaria surveillance system for elimination, termed the Malaria Case Notification (MCN) system in 2012. MCN relies on cell phone reporting to transmit data on all malaria cases detected at health facilities, and tracks all positive cases to their household to test all household members for malaria. As MCN is the core of Zanzibar's public health enterprise for malaria elimination, it should periodically undergo a performance evaluation. Following recommendations in the Centers for Disease Control and Prevention's (CDC's) Updated Guidelines for Evaluating Public Health Surveillance Systems...

Prediction and nonparametric estimation for time series analysis with heavy tails

Hall, Peter; Peng, L; Yao, Qiwei
Fonte: Blackwell Publishing Ltd Publicador: Blackwell Publishing Ltd
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
65.85%
Motivated by prediction problems for time series with heavy-tailed marginal distributions, we consider methods based on 'local least absolute deviations' for estimating a regression median from dependent data. Unlike more conventional 'local median' methods, which are in effect based on locally fitting a polynomial of degree 0, techniques founded on local least absolute deviations have quadratic bias right up to the boundary of the design interval. Also in contrast to local least-squares methods based on linear fits, the order of magnitude of variance does not depend on tail-weight of the error distribution. To make these points clear, we develop theory describing local applications to time series of both least-squares and least-absolute-deviations methods, showing for example that, in the case of heavy-tailed data, the conventional local-linear least-squares estimator suffers from an additional bias term as well as increased variance.

Using difference-based methods for inference in nonparametric regression with time series errors

Hall, Peter; Van Keilegom, Ingrid
Fonte: Aiden Press Publicador: Aiden Press
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
65.97%
We show that difference-based methods can be used to construct simple and explicit estimators of error covariance and autoregressive parameters in nonparametric regression with time series errors. When the error process is Gaussian our estimators are efficient, but they are available well beyond the Gaussian case. As an illustration of their usefulness we show that difference-based estimators can be used to produce a simplified version of time series cross-validation. This new approach produces a bandwidth selector that is equivalent, to both first and second orders, to that given by the full time series cross-validation algorithm. Other applications of difference-based methods are to variance estimation and construction of confidence bands in nonparametric regression.

Detecting a global warming signal in hemispheric temperature series: a structural time series analysis

Stern, David; Kaufmann, R K
Fonte: Kluwer Academic Publishers Publicador: Kluwer Academic Publishers
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
65.84%
Non-stationary time series such as global and hemispheric temperatures, greenhouse gas concentrations, solar irradiance, and anthropogenic sulfate aerosols, may contain stochastic trends (the simplest stochastic trend is a random walk) which, due to their unique patterns, can act as a signal of the influence of other variables on the series in question. Two or more series may share a common stochastic trend, which indicates that either one series causes the behavior of the other or that there is a common driving variable. Recent developments in econometrics allow analysts to detect and classify such trends and analyze relationships among series that contain stochastic trends. We apply some univariate autoregression based tests to evaluate the presence of stochastic trends in several time series for temperature and radiative forcing. The temperature and radiative forcing series are found to be of different orders of integration which would cast doubt on the anthropogenic global warming hypothesis. However, these tests can suffer from size distortions when applied to noisy series such as hemispheric temperatures. We, therefore, use multivariate structural time series techniques to decompose Northern and Southern Hemisphere temperatures into stochastic trends and autoregressive noise processes. These results show that there are two independent stochastic trends in the data. We investigate the possible origins of these trends using a regression method. Radiative forcing due to greenhouse gases and solar irradiance can largely explain the common trend. The second trend...

Parallel Approach for Time Series Analysis with General Regression Neural Networks

Cuevas-Tello,J.C.; González-Grimaldo,R.A.; Rodríguez-González,O.; Pérez-González,H.G.; Vital-Ochoa,O.
Fonte: UNAM, Centro de Ciencias Aplicadas y Desarrollo Tecnológico Publicador: UNAM, Centro de Ciencias Aplicadas y Desarrollo Tecnológico
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/04/2012 Português
Relevância na Pesquisa
65.86%
The accuracy on time delay estimation given pairs of irregularly sampled time series is of great relevance in astrophysics. However the computational time is also important because the study of large data sets is needed. Besides introducing a new approach for time delay estimation, this paper presents a parallel approach to obtain a fast algorithm for time delay estimation. The neural network architecture that we use is general Regression Neural Network (GRNN). For the parallel approach, we use Message Passing Interface (MPI) on a beowulf-type cluster and on a Cray supercomputer and we also use the Compute Unified Device Architecture (CUDA™) language on Graphics Processing Units (GPUs). We demonstrate that, with our approach, fast algorithms can be obtained for time delay estimation on large data sets with the same accuracy as state-of-the-art methods.