scholarly journals The cubic regression model of thermal estimation in the flammability test of the fibrous compound used in bus bodies

2019 ◽  
Vol 264 ◽  
pp. 02004
Author(s):  
Cristian Pérez-Salinas ◽  
Christian Castro ◽  
Roberto Valencia
Keyword(s):  
Composite Material ◽  
Regression Model ◽  
Thermal Behavior ◽  
Regression Models ◽  
Fire Behavior ◽  
Regression Coefficients ◽  
Automotive Applications ◽  
Iso Standard ◽  
Flammability Test ◽  
Best Fit

The fire behavior of fiber compounds is a serious concern in automotive applications. The objective of the proposed work is to predict thermal behavior during flammability testing of the composite material of polymer matrix reinforced with fiberglass used in the interior lining of bodies. Different regression models were performed to determine the best fit. This regression analysis determines the existing correlation between the acquired parameters of burn time and temperature versus two types of fibers used for the interior decorative lining. Different regression coefficients were determined and used for the prediction. Through the best fit regression model, thermal behavior of burning during the flammability test is predicted under ISO standard 3795: 1989 and FMVSS 302. The prediction was made for two types of composites, Roof Fiber, FT, and Fiber for laterals, FL. The cubic regression model showed the best prediction fit with a Rq of 0.79 for FT and 0.81 for FL.

scholarly journals Impact of Women Empowerment on Infant Mortality Rate – Spatial Lag and Error Regression Model

2020 ◽  
Vol 2 (6) ◽  
Author(s):  
Kanwal Preet K. Gill ◽  
Priyanka Devgun ◽  
Amanpreet Kaur
Keyword(s):  
Mortality Rate ◽  
Infant Mortality ◽  
Regression Model ◽  
Mobile Phone ◽  
Regression Models ◽  
Institutional Delivery ◽  
Infant Mortality Rate ◽  
Women Empowerment ◽  
Spatial Lag ◽  
Best Fit

Background: Despite significant progress in recent decade, the burden of infant mortality in India remains high. As women is the main caregiver to the infant, women empowerment may be the important determinant of infant mortality rate which has received little attention. Therefore, the current study was planned to study the impact of women empowerment on infant mortality and to find the best fit regression model to predict infant mortality rate in various states of India. Methods: The National Family Health Survey (NFHS) 2015-16 data was used and infant mortality rate in various states and union territories of India was mapped. Regression analysis was performed with infant mortality rate as the dependent variable. The exploratory variables (Percentages) used for women empowerment were the literacy among women, women who opted for institutional delivery, women owning a house and/or land (alone or jointly with others), women having a bank or saving account and women having mobile phone that they themselves used. Ordinary Least Square Regression and spatial lag and error regression models were applied. Infant mortality rates predicted by using the best fit model were mapped. Results: Institutional delivery, owning a bank account and availability of personal mobile phone showed statistically significant inverse relation with infant mortality rate. Out of three regression models, spatial lag regression model was found to be the best fit model to predict infant mortality rate. The northern and eastern states were predicted to have highest infant mortality rate. Conclusion: As northern and eastern states were predicted to have highest infant mortality rate, further studies at individual level may be planned to find out the factors in these regions.

scholarly journals Assessment of spline functions for estimating growth curve parameters of FUNAABAlpha chickens

2020 ◽  
Vol 46 (5) ◽  
Author(s):  
H. A. Bashiru ◽  
S. O. Oseni ◽  
L. A. Omadime
Keyword(s):  
Regression Models ◽  
Selection Criteria ◽  
Goodness Of Fit ◽  
Information Criterion ◽  
Regression Coefficients ◽  
Spline Functions ◽  
Linear Regression Models ◽  
Male And Female ◽  
Spline Models ◽  
Best Fit

The objective of this study was to fit four spline linear regression models to describe the growth of FUNAAB-Alpha Chickens (FAC). Body weight records of 300 FAC raised from day old till the 20th week were used to fit spline models of 3 (SP3), 4 (SP4), 5 (SP5) and 6 knots (SP6) using the REG procedure of SAS®. The data were first plotted to determine the most appropriate location of knots and they were placed at 4th, 10 th and 16 th week of age for SP3; 4th, 8th, 12th and 16th week for SP4; 4th, 7th, 10th, 14th and 18th week for SP5 and 3rd, 6th, 9th, 12th, 15 th and 18 th week for SP6, respectively. The hatch weight predicted by SP3 was observed to be highest while SP6 predicted the lowest hatch weight for male and female FAC. Regression coefficients ranged from -38.47 to 47.46 and -39.40 to 40.47 for the male and female, respectively. For all the models, the highest magnitude of these coefficients were estimated at early ages after hatching (at 3 to 10 weeks of age). Based on Bayesian Information Criterion (BIC) and Akaike Information Criterion (AIC) as the goodness-of-fit selection criteria, SP3 had the lowest value for AIC and BIC for male FAC while SP4 had the lowest value of AIC and BIC for the female FAC. It was concluded that spline models of lower knots (SP3 and SP4) were the best fit to describe the growth of male and female FAC respectively, and that growth rate at early stages of life of FAC may be good predictors of later growth performance.

Robustness against outliers: A new variance inflated regression model for proportions

2019 ◽  
Vol 20 (3) ◽  
pp. 274-309
Author(s):  
Agnese Maria Di Brisco ◽  
Sonia Migliorati ◽  
Andrea Ongaro
Keyword(s):  
Regression Model ◽  
Bayesian Approach ◽  
Regression Models ◽  
The Other ◽  
Regression Coefficients ◽  
Posterior Distributions ◽  
New Model ◽  
Model Based ◽  
Regression Curves ◽  
New Distribution

This article addresses the issue of building regression models for bounded responses, which are robust in the presence of outliers. To this end, a new distribution on (0,1) and a regression model based on it are proposed and some properties are derived. The distribution is a mixture of two beta components. One of them, showing a higher variance (variance inflated) is expected to capture outliers. Within a Bayesian approach, an extensive robustness study is performed to compare the new model with three competing ones present in the literature. A broad range of inferential tools are considered, aimed at measuring the influence of various outlier patterns from diverse perspectives. It emerges that the new model displays a better performance in terms of stability of regression coefficients’ posterior distributions and of regression curves under all outlier patterns. Moreover, it exhibits an adequate behaviour under all considered settings, unlike the other models.

scholarly journals Use of a Big Data Analysis in Regression of Solar Power Generation on Meteorological Variables for a Korean Solar Power Plant

2021 ◽  
Vol 11 (4) ◽  
pp. 1776
Author(s):  
Young Seo Kim ◽  
Han Young Joo ◽  
Jae Wook Kim ◽  
So Yun Jeong ◽  
Joo Hyun Moon
Keyword(s):  
Power Generation ◽  
Data Analysis ◽  
Power Plant ◽  
Relative Humidity ◽  
Regression Model ◽  
Regression Models ◽  
Solar Power ◽  
Meteorological Variables ◽  
Solar Power Plant ◽  
Solar Power Generation

This study identified the meteorological variables that significantly impact the power generation of a solar power plant in Samcheonpo, Korea. To this end, multiple regression models were developed to estimate the power generation of the solar power plant with changing weather conditions. The meteorological data for the regression models were the daily data from January 2011 to December 2019. The dependent variable was the daily power generation of the solar power plant in kWh, and the independent variables were the insolation intensity during daylight hours (MJ/m2), daylight time (h), average relative humidity (%), minimum relative humidity (%), and quantity of evaporation (mm). A regression model for the entire data and 12 monthly regression models for the monthly data were constructed using R, a large data analysis software. The 12 monthly regression models estimated the solar power generation better than the entire regression model. The variables with the highest influence on solar power generation were the insolation intensity variables during daylight hours and daylight time.

scholarly journals Regression Models in Complex Survey Sampling for Sensitive Quantitative Variables

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 609
Author(s):  
María del Mar Rueda ◽  
Beatriz Cobo ◽  
Antonio Arcos
Keyword(s):  
Regression Analysis ◽  
Regression Models ◽  
Regression Coefficients ◽  
Randomized Response ◽  
Statistical Tool ◽  
Complex Survey ◽  
Complex Sample ◽  
Qualitative Variables ◽  
World Survey ◽  
Sensitive Variable

Randomized response (RR) techniques are widely used in research involving sensitive variables, such as drugs, violence or crime, especially when a population mean or prevalence must be estimated. However, they are not generally applied to examine relationships between a sensitive variable and other characteristics. This type of technique was initially applied to qualitative variables, and studies later showed that a logistic regression may be performed with RR data. Since many of the variables considered in this context are quantitative, RR techniques were extended to these cases to estimate the values required. Regression analysis is a valuable statistical tool for exploring relationships among variables and for establishing associations between responses and covariates. In this article, we propose a design-based regression analysis for complex sample designs based on the unified RR approach. We present estimators of the regression coefficients, study their theoretical properties and consider different ways to estimate their variance. The properties of these estimation techniques were simulated using various quantitative randomized models. The method proposed was also used to analyse the findings from a real-world survey.

scholarly journals Analysis models for variables associated with breastfeeding duration

2013 ◽  
Vol 31 (3) ◽  
pp. 306-314 ◽  
Author(s):  
Edson Theodoro dos S. Neto ◽  
Eliana Zandonade ◽  
Adauto Oliveira Emmerich
Keyword(s):  
Logistic Regression ◽  
Protective Factors ◽  
Regression Model ◽  
Odds Ratio ◽  
Regression Models ◽  
Adjusted Odds Ratio ◽  
Cox Regression ◽  
Bottle Feeding ◽  
Breastfeeding Duration ◽  
Factors Associated

OBJECTIVE To analyze the factors associated with breastfeeding duration by two statistical models. METHODS A population-based cohort study was conducted with 86 mothers and newborns from two areas primary covered by the National Health System, with high rates of infant mortality in Vitória, Espírito Santo, Brazil. During 30 months, 67 (78%) children and mothers were visited seven times at home by trained interviewers, who filled out survey forms. Data on food and sucking habits, socioeconomic and maternal characteristics were collected. Variables were analyzed by Cox regression models, considering duration of breastfeeding as the dependent variable, and logistic regression (dependent variables, was the presence of a breastfeeding child in different post-natal ages). RESULTS In the logistic regression model, the pacifier sucking (adjusted Odds Ratio: 3.4; 95%CI 1.2-9.55) and bottle feeding (adjusted Odds Ratio: 4.4; 95%CI 1.6-12.1) increased the chance of weaning a child before one year of age. Variables associated to breastfeeding duration in the Cox regression model were: pacifier sucking (adjusted Hazard Ratio 2.0; 95%CI 1.2-3.3) and bottle feeding (adjusted Hazard Ratio 2.0; 95%CI 1.2-3.5). However, protective factors (maternal age and family income) differed between both models. CONCLUSIONS Risk and protective factors associated with cessation of breastfeeding may be analyzed by different models of statistical regression. Cox Regression Models are adequate to analyze such factors in longitudinal studies.

A Unifying Classification for Functional Regression Modeling

2018 ◽  
Author(s):  
Frédéric Ferraty ◽  
Philippe Vieu
Keyword(s):  
Regression Model ◽  
Regression Models ◽  
Functional Model ◽  
Additive Functional ◽  
Regression Modeling ◽  
Functional Regression ◽  
Regression Problem ◽  
Nonparametric Models ◽  
Index Model ◽  
Functional Linear Regression

This article presents a unifying classification for functional regression modeling, and more specifically for modeling the link between two variables X and Y, when the explanatory variable (X) is of a functional nature. It first provides a background on the proposed classification of regression models, focusing on the regression problem and defining parametric, semiparametric, and nonparametric models, and explains how semiparametric modeling can be interpreted in terms of dimension reduction. It then gives four examples of functional regression models, namely: functional linear regression model, additive functional regression model, smooth nonparametric functional model, and single functional index model. It also considers a number of new models, directly adapted to functional variables from the existing standard multivariate literature.

scholarly journals Genotypic parameters for egg production in pure breed hens by using random regression model

2017 ◽  
Vol 47 (5) ◽  
Author(s):  
Priscila Becker Ferreira ◽  
Paulo Roberto Nogara Rorato ◽  
Fernanda Cristina Breda ◽  
Vanessa Tomazetti Michelotti ◽  
Alexandre Pires Rosa ◽  
...  
Keyword(s):  
Regression Model ◽  
Regression Models ◽  
Egg Production ◽  
Random Regression ◽  
Broad Sense Heritability ◽  
Residual Matrix ◽  
Random Regression Model ◽  
Residual Covariance ◽  
Genotypic Correlations ◽  
Selection Of

ABSTRACT: This study aimed to test different genotypic and residual covariance matrix structures in random regression models to model the egg production of Barred Plymouth Rock and White Plymouth Rock hens aged between 5 and 12 months. In addition, we estimated broad-sense heritability, and environmental and genotypic correlations. Six random regression models were evaluated, and for each model, 12 genotypic and residual matrix structures were tested. The random regression model with linear intercept and unstructured covariance (UN) for a matrix of random effects and unstructured correlation (UNR) for residual matrix adequately model the egg production curve of hens of the two study breeds. Genotypic correlations ranged from 0.15 (between age of 5 and 12 months) to 0.99 (between age of 10 and 11 months) and increased based on the time elapsed. Egg production heritability between 5- and 12-month-old hens increased with age, varying from 0.15 to 0.51. From the age of 9 months onward, heritability was moderate with estimates of genotypic correlations higher than 90% at the age of 10, 11, and 12 months. Results suggested that selection of hens to improve egg production should commence at the ninth month of age.

scholarly journals KAJIAN METODE ORDINARY LEAST SQUARE DAN ROBUST ESTIMASI M PADA MODEL REGRESI LINIER SEDERHANA YANG MEMUAT OUTLIER

2020 ◽  
Vol 11 (1) ◽  
pp. 21
Author(s):  
Zahrotul Aflakhah ◽  
Jajang Jajang ◽  
Agustini Tripena Br. Sb.
Keyword(s):  
Full Article ◽  
Regression Model ◽  
Sample Size ◽  
Regression Models ◽  
Estimation Method ◽  
Ordinary Least Squares ◽  
Least Square ◽  
Simple Linear Regression ◽  
Ordinary Least Square ◽  
M Estimation

This research discusses about the Ordinary Least Squares (OLS) method and robust M-estimation method; compare between the Tukey bisquare and Huber weighting from simple linier regression models that contain outliers. Data are generated through simulation with the percentages of outliers and sample sizes. Each data will be formed into a simple linier regression model, then the percentage of outliers, RSE and MAD values are calculated. The results show that RSE and MAD values produced by a simple linear regression model with the OLS method are influenced by the percentage of outliers. However, the regression model of robust M-estimation with sample size 30, 60, 90, 120, and 150 results an unstable RSE values with the change of the percentage of outlier and the MAD values that are not affected by the percentage of outliers and sample size. The robust M-estimation method with Tukey Bisquare weighting is as good as the Huber weighting. Full Article

scholarly journals Real-Time Prediction of the COVID-19 Epidemic in Thailand using Simple Model-Free Method and Time Series Regression Model

2021 ◽  
Vol 18 (14) ◽  
Author(s):  
Rati WONGSATHAN
Keyword(s):  
Genetic Algorithm ◽  
Time Series ◽  
Regression Model ◽  
Regression Models ◽  
Gaussian Function ◽  
Logistic Function ◽  
Hyperbolic Tangent ◽  
Time Series Regression ◽  
Model Free ◽  
Tangent Function

The novel coronavirus 2019 (COVID-19) pandemic was declared a global health crisis. The real-time accurate and predictive model of the number of infected cases could help inform the government of providing medical assistance and public health decision-making. This work is to model the ongoing COVID-19 spread in Thailand during the 1st and 2nd phases of the pandemic using the simple but powerful method based on the model-free and time series regression models. By employing the curve fitting, the model-free method using the logistic function, hyperbolic tangent function, and Gaussian function was applied to predict the number of newly infected patients and accumulate the total number of cases, including peak and viral cessation (ending) date. Alternatively, with a significant time-lag of historical data input, the regression model predicts those parameters from 1-day-ahead to 1-month-ahead. To obtain optimal prediction models, the parameters of the model-free method are fine-tuned through the genetic algorithm, whereas the generalized least squares update the parameters of the regression model. Assuming the future trend continues to follow the past pattern, the expected total number of patients is approximately 2,689 - 3,000 cases. The estimated viral cessation dates are May 2, 2020 (using Gaussian function), May 4, 2020 (using a hyperbolic function), and June 5, 2020 (using a logistic function), whereas the peak time occurred on April 5, 2020. Moreover, the model-free method performs well for long-term prediction, whereas the regression model is suitable for short-term prediction. Furthermore, the performances of the regression models yield a highly accurate forecast with lower RMSE and higher R2 up to 1-week-ahead. HIGHLIGHTS COVID-19 model for Thailand during the first and second phases of the epidemic The model-free method using the logistic function, hyperbolic tangent function, and Gaussian function  applied to predict the basic measures of the outbreak Regression model predicts those measures from one-day-ahead to one-month-ahead The parameters of the model-free method are fine-tuned through the genetic algorithm  GRAPHICAL ABSTRACT

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