scholarly journals Um modelo alternativo de risco para companhias não-financeiras aplicado ao setor brasileiro de papel e celulose

2009 ◽  
Vol 7 (3) ◽  
pp. 347
Author(s):  
Hsia Hua Sheng ◽  
Cristiane Karcher ◽  
Paulo Hubert Jr.
Keyword(s):  
At Risk ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Financial Risk ◽  
Risk Measure ◽  
Multiple Linear Regression Model ◽  
Principal Component ◽  
Linear Regression Models ◽  
Explanatory Factors ◽  
Paper And Pulp

Earnings at Risk (EaR) is a financial risk measure that can be applied to non-financial companies, similarly to Cash Flow at Risk (CFaR). It is based on a relation that can be quantified using a multiple linear regression model, where the dependent variable is the change on the company's results and the independent variables are changes in distinct risk factors. The presence of correlation between explanatory factors (multicollinearity) in this kind of model may cause problems when calculating EaR and CFaR. In this paper, we indicate some possible consequences of these problems when calculating EaR, and propose a method to solve it based on Principal Component Analysis technique. To test the model, we choose the Brazilian agriculture-business industry, more specifically the paper and pulp sectors. We will show that, on the absence of significant correlation between variables, the proposed model has equivalent performance to usual multiple linear regression models. We find evidence that when correlation appears, the model here proposed yields more accurate and reliable forecasts.

Modeling of TEC over the Iberian Peninsula using PCA decomposition and multiple linear regression on space weather parameters

2021 ◽  
Author(s):  
Anna Morozova ◽  
Tatiana Barlyaeva ◽  
Teresa Barata
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Iberian Peninsula ◽  
Space Weather ◽  
Multiple Linear Regression Model ◽  
Principal Component ◽  
Electron Content ◽  
Time Lags ◽  
Total Electron ◽  
Weather Parameters

<p>The total electron content (TEC) over the Iberian Peninsula was modeled using a three-step procedure. At the 1<sup>st</sup> step the TEC series is decomposed using the principal component analysis (PCA) into several daily modes. Then, the amplitudes of those daily modes is fitted by a multiple linear regression model (MRM) using several types of space weather parameters as regressors. Finally, the TEC series is reconstructed using the PCA daily modes and MRM fitted amplitudes.</p><p>The advantage of such approach is that seasonal variations of the TEC daily modes are automatically extracted by PCA. As space weather parameters we considered proxies for the solar UV and XR fluxes, number of the solar flares, parameters of the solar wind and the interplanetary magnetic field, and geomagnetic indices. Different time lags and combinations of the regressors are tested.</p><p>The possibility to use such TEC models for forecasting was tested. Also, a possibility to use neural networks (NN) instead of MRM is studied.</p>

scholarly journals Pedestrian crossing model in urban street (Case study on commercial area in Bandung)

2018 ◽  
Vol 181 ◽  
pp. 02004
Author(s):  
Sony Sulaksono Wibowo ◽  
Rian Wicaksana
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Group Size ◽  
Multiple Linear Regression Model ◽  
Delay Model ◽  
Affecting Factors ◽  
Linear Regression Models ◽  
Urban Street ◽  
Commercial Area ◽  
Pedestrian Crossing

Pedestrians who cross without any crossing facilities and under mixed-traffic tend to have varying responses. The responses can be analyzed by using multiple linear regression model, with pedestrian crossing delay and pedestrian crossing speed set as response variables. This research aims to develop two pedestrian crossing models based on the condition at the midblock part of urban street, in particular commercial area and without specific crossing facilities. The two models are pedestrian crossing delay model and pedestrian crossing speed model. The affecting factors are considered in linear relationship and the multiple-linear regression models are used. The principal factor in the pedestrian crossing delay model is group size of more than 3 persons, while in the model of pedestrian crossing speed, the principal factors are number of group size and pedestrian baggage. The mean of pedestrian crossing delay was about 3 seconds while pedestrian crossing speed was about 1 m/s.

scholarly journals Multiple Linear Regression Model of Meningococcal Disease in Ukraine: 1992–2015

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Hennadii Mokhort
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Meningococcal Disease ◽  
Total Population ◽  
Linear Models ◽  
National Level ◽  
Multiple Linear Regression Model ◽  
National Database ◽  
Linear Regression Models ◽  
Susceptible Population

Estimating the rates of invasive meningococcal disease (IMD) from epidemiologic data remains critical for making public health decisions. In Ukraine, such estimations have not been performed. We used epidemiological data to develop a national database. These data were used to estimate the population susceptible to IMD and identify the prevalence of asymptomatic carriers of N. meningitidis using simple epidemiological models of meningococcal disease that may be used by the national policy makers. The goal was to create simple, easily understood analysis of patterns of the infection within Ukraine that would capture the major features of the infection dynamics. Studies used nationally reported data during 1992–2015. A logic model identified the prevalence of carriage and the proportion of the population susceptible to IMD as key drivers of IMD incidence. Multiple linear regression models for all ages (total population) and for children ≤14 years old were fit to national-level data. Linear models with the incidence of IMD as an outcome were highly associated with carriage and estimated susceptible population in both total population and children (R2 = 0.994 and R2 = 0.978, respectively). The susceptibility rate to IMD in the study total population averaged 0.0034 ± 0.0009% annually. At the national level, IMD can be characterized by the simple interaction between the prevalence of asymptomatic carriage and the proportion of the susceptible population. IMD association with prevalence rates of carriage and the proportion of susceptible population is sufficiently strong for national-level planning of intervention strategies for IMD.

The Research of Wine Quality Evaluation Based on Multiple Linear Regression

2013 ◽  
Vol 756-759 ◽  
pp. 2489-2493
Author(s):  
Huai Hui Liu ◽  
Wen Long Ji ◽  
Peng Zhang ◽  
Chuan Wen Yao
Keyword(s):  
Principal Component Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Quality Evaluation ◽  
Evaluation Model ◽  
Multiple Linear Regression Model ◽  
Principal Component ◽  
Wine Grape ◽  
Wine Quality

Through the establishment of evaluation model based on principal component analysis, select 8 principal components from nearly 30 indexes of wine grape. Then we establish the multiple linear regression model and analyse the association between physicochemical indexes of wine grape and wine, and the influence of physicochemical indexes of wine grape and wine on wine quality. Finally study whether we could use the physicochemical indexes to evaluate the wine quality.

scholarly journals Research on a Seepage Monitoring Model of a High Core Rockfill Dam Based on Machine Learning

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2749 ◽  
Author(s):  
Xiang Cheng ◽  
Qingquan Li ◽  
Zhiwei Zhou ◽  
Zhixiang Luo ◽  
Ming Liu ◽  
...  
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Influence Factors ◽  
Multiple Linear Regression Model ◽  
Principal Component ◽  
Random Errors ◽  
Monitoring Model ◽  
Rockfill Dam ◽  
Core Rockfill Dam

The seepage of a rockfill dam with a high core wall is an important and difficult issue in the safety monitoring of a core rockfill dam, something about which managers are immensely concerned. Seepage of a high core rockfill dam is mainly affected by factors such as water level, rainfall, temperature, filling height, and aging. The traditional research method is to establish a multiple linear regression model to analyze the influence factors of seepage. However, the multicollinearity between these factors affects parameter estimation, and random errors in the data cause the regression model to fail to be established. This paper starts with data collected by an osmometer, uses the 3δ criterion to process the outliers in the sample data, uses the R language to perform principal component analysis on the processed data to eliminate the multicollinearity of the factors, and finally uses multiple linear regression to model and analyze the data. Taking the Nuozhadu high core rockfill dam as an example, the influencing factors of seepage in the construction period and the impoundment period were studied and the seepage was then forecasted. This method provides guidance for further studies of the same type of dam seepage monitoring model.

scholarly journals Simulation of wheat biomass yield by thermal time, rainfall and nitrogen

2017 ◽  
Vol 21 (11) ◽  
pp. 763-768 ◽  
Author(s):  
Ana P. B. Trautmann ◽  
José A. G. da Silva ◽  
Manuel O. Binelo ◽  
Osmar B. Scremin ◽  
Ângela T. W De Mamann ◽  
...  
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Biomass Yield ◽  
Multiple Linear Regression Model ◽  
Thermal Time ◽  
Linear Regression Models ◽  
Split Application ◽  
N Supply

ABSTRACT Wheat biomass yield focused on the production of quality silage is dependent on rainfall, temperature and nitrogen (N). The objective of the study was to validate the use of rainfall, thermal time and N as potential variables for the composition of the multiple linear regression model and simulation of wheat biomass yield for silage production under N supply conditions during the cycle, in the systems of succession. The study was conducted in 2012, 2013 and 2014, in randomized blocks with four replicates in 4 x 3 factorial, for N-fertilizer doses (0, 30, 60, 120 kg ha-1) and forms of N supply [single application (100%) in the stage V3 (third expanded leaf); split application (70%/30%) in the stages V3/V6 (third and sixth expanded leaves); split application (70%/30%) in the stages V3/E (third expanded leaf and beginning of grain filling)], respectively, in the systems soybean/wheat and maize/wheat. Rainfall and N are potential variables in the composition of the multiple linear regression model. Multiple linear regression models are efficient in the simulation of wheat biomass yield for silage under the N supply conditions during the cycle in the succession systems.

scholarly journals Determinants of Knowledge and Attitude towards Breastfeeding in Rural Pregnant Women Using Validated Instruments in Ethiopia

2021 ◽  
Vol 18 (15) ◽  
pp. 7930
Author(s):  
Misra Abdulahi ◽  
Atle Fretheim ◽  
Alemayehu Argaw ◽  
Jeanette H. Magnus
Keyword(s):  
Linear Regression ◽  
Pregnant Women ◽  
Multiple Linear Regression ◽  
Multiple Linear Regression Model ◽  
Educational Interventions ◽  
Attitude Scale ◽  
Linear Regression Models ◽  
Breastfeeding Knowledge ◽  
Breastfeeding Practices ◽  
Knowledge And Attitude

Understanding the underlying determinants of maternal knowledge and attitude towards breastfeeding guides the development of context-specific interventions to improve breastfeeding practices. This study aimed to assess the level and determinants of breastfeeding knowledge and attitude using validated instruments in pregnant women in rural Ethiopia. In total, 468 pregnant women were interviewed using the Afan Oromo versions of the Breastfeeding Knowledge Questionnaire (BFKQ-AO) and the Iowa Infant Feeding Attitude Scale (IIFAS-AO). We standardized the breastfeeding knowledge and attitude scores and fitted multiple linear regression models to identify the determinants of knowledge and attitude. 52.4% of the women had adequate knowledge, while 60.9% of the women had a neutral attitude towards breastfeeding. In a multiple linear regression model, maternal occupation was the only predictor of the BFKQ-AO score (0.56SD; 95%CI, 1.28, 4.59SD; p = 0.009). Age (0.57SD; 95%CI, 0.24, 0.90SD; p = 0.001), parity (−0.24SD; 95%CI, −0.47, −0.02SD; p = 0.034), antenatal care visits (0.41SD; 95%CI, 0.07, 0.74SD; p = 0.017) and the BFKQ-AO score (0.08SD; 95% CI, 0.06, 0.09SD; p < 0.000) were predictors of the IIFAS-AO score. Nearly half of the respondents had inadequate knowledge and most women had a neutral attitude towards breastfeeding. Policymakers and managers could address these factors when planning educational interventions to improve breastfeeding practices.

Applications of a Multiple Linear Regression Model to the Analysis of Relationships between Eastward- and Westward-Moving Intraseasonal Modes

2004 ◽  
Vol 61 (24) ◽  
pp. 3041-3048 ◽  
Author(s):  
Paul E. Roundy ◽  
William M. Frank
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Spatial Scales ◽  
Statistical Significance ◽  
Multiple Linear Regression Model ◽  
Longwave Radiation ◽  
Linear Regression Models ◽  
Linear Predictor

Abstract Multiple linear regression models with nonlinear power terms may be applied to find relationships between interacting wave modes that may be characterized by different frequencies. Such regression techniques have been explored in other disciplines, but they have not been used in the analysis of atmospheric circulations. In this study, such a model is developed to predict anomalies of westward-moving intraseasonal precipitable water by utilizing the first through fourth powers of a time series of outgoing longwave radiation that is filtered for eastward propagation and for the temporal and spatial scales of the tropical intraseasonal oscillations. An independent and simpler compositing method is applied to show that the results of this multiple linear regression model provide a better description of the actual relationships between eastward- and westward-moving intraseasonal modes than a regression model that includes only the linear predictor. A statistical significance test is applied to the coefficients of the multiple linear regression model, and they are found to be significant over broad regions of the Tropics. Correlations between the predictors are shown to not significantly influence results for this case. Results show that this regression model reveals physical relationships between eastward- and westward-moving intraseasonal modes. The physical interpretation of these regression relationships is given in a companion paper.

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