Matemática e Estatística / Mathematics and Statistics
Permanent URI for this community
Browse
Browsing Matemática e Estatística / Mathematics and Statistics by Sustainable Development Goals (SDG) "17:Parcerias para a Implementação dos Objetivos"
Now showing 1 - 3 of 3
Results Per Page
Sort Options
- Comparison of discriminant analysis methods: Application to occupational exposure to particulate matterPublication . Ramos, Maria do Rosário; Carolino, E.; Viegas, Carla; Viegas, SandraHealth effects associated with occupational exposure to particulate matter have been studied by several authors. In this study were selected six industries of five different areas: Cork company 1, Cork company 2, poultry, slaughterhouse for cattle, riding arena and production of animal feed. The measurements tool was a portable device for direct reading. This tool provides information on the particle number concentration for six different diameters, namely 0.3 μm, 0.5 μm, 1 μm, 2.5 μm, 5 μm and 10 μm. The focus on these features is because they might be more closely related with adverse health effects. The aim is to identify the particles that better discriminate the industries, with the ultimate goal of classifying industries regarding potential negative effects on workers' health. Several methods of discriminant analysis were applied to data of occupational exposure to particulate matter and compared with respect to classification accuracy. The selected methods were linear discriminant analyses (LDA); linear quadratic discriminant analysis (QDA), robust linear discriminant analysis with selected estimators (MLE (Maximum Likelihood Estimators), MVE (Minimum Volume Elipsoid), "t", MCD (Minimum Covariance Determinant), MCD-A, MCD-B), multinomial logistic regression and artificial neural networks (ANN). The predictive accuracy of the methods was accessed through a simulation study. ANN yielded the highest rate of classification accuracy in the data set under study. Results indicate that the particle number concentration of diameter size 0.5 μm is the parameter that better discriminates industries.
- Improving forecasting by resampling STL decompositionPublication . Cordeiro, Clara; Ramos, Maria do Rosário; Neves, M. ManuelaThe development of new forecasting algorithms has shown an increasing interest due to the emerging of new fields of application like machine learning and forecasting competitions. Although initially intended for independent random variables, bootstrap methods can be successfully applied to time series. The Boot.EXPOS procedure, which combines bootstrap and exponential smoothing methods, has shown promising results for forecasting. This work proposes a new approach to forecasting, which is briefly described as follows: using Seasonal-Trend decomposition by Loess (STL), the best STL fit is selected by testing all possible combinations of parameters. The best combination of smoothing parameters is chosen based on an accuracy measure. The time series is then decomposed into components according to the best STL fit. The Boot.EXPOS procedure is employed to forecast the seasonal component and the seasonally adjusted time series. These forecasts are aggregated to obtain a final forecast. The performance of this combined forecast is evaluated using real datasets and compared with other established forecasting methods.
- Uncovering abnormal water consumption patterns for sustainability’s sake: a statistical approachPublication . Borges, Ana; Cordeiro, Clara; Ramos, Maria do RosárioMonitoring domestic water usage may help the water utilities uncover abnormal water consumption. In this context, it is necessary to improve and develop tools based on data analysis of households’ meter readings. This study contributes to this goal by using a statistical methodology that detects abnormal water consumption patterns, namely, significant increases or decreases. This approach relies on a combination of methods that analyse billed water consumption time series. The first step is to decompose the time series using Seasonal-Trend decomposition based on Loess. Next, breakpoint analysis is performed on the seasonally adjusted time series to look for changes in the pattern over time. Afterwards, the Mann–Kendall test and Sen’s slope estimator are applied to assess whether there are significant increases or decreases in water consumption. The strategy is applied to water consumption data from the Algarve, Portugal, successfully detecting breakpoints associated with significant increasing or decreasing trends.