scholarly journals Estimación de la esperanza de vida a nivel municipal y por marginación sociodemográfica: una aplicación del método de Swanson para el caso de México, 2010 / Estimation of life expectancy at the municipal level and sociodemographic marginalization: Using the Swanson method for the case of Mexico, 2010

2016 ◽  
Vol 32 (1) ◽  
pp. 97
Author(s):  
Israel Paredes ◽  
Eliud Silva
Keyword(s):  
Life Expectancy ◽  
Regression Model ◽  
Nonlinear Regression ◽  
Nonlinear Regression Model ◽  
Life Expectancy At Birth ◽  
Estimate Life Expectancy ◽  
Municipal Level

Se presenta la aplicación del método de Swanson, modelo de regresión no lineal, para estimar la esperanza de vida al nacer para el caso de México en 2010, tanto a nivel municipal como por grado de marginación sociodemográfica. Se evidencia la simplicidad del método a través de exponer los pocos insumos demográficos que requiere, y se concluye con el potencial y precisión de la herramienta por grado de marginación, así como con la coherencia que se alcanza al hacer comparaciones entre las estimaciones obtenidas en la aplicación y los datos que se tienen de manera oficial del indicador.AbstractThe Swanson method, a nonlinear regression model, is used to estimate life expectancy at birth for the case of Mexico 2010, at both the municipal level and because of the sociodemographic degree of marginalization. The authors show the simplicity of the method by explaining the few demographic inputs required, and concludes with the potential and accuracy of the tool due to the degree of marginalization, as well as the consistency achieved when making comparisons between the estimates obtained in the application and the data obtained from the official indicator.

scholarly journals Dimension Analysis-Based Model for Prediction of Shale Compressive Strength

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Xiangyu Fan ◽  
Fenglin Xu ◽  
Lin Chen ◽  
Qiao Chen ◽  
Zhiwei Liu ◽  
...  
Keyword(s):  
Compressive Strength ◽  
Regression Model ◽  
Nonlinear Regression ◽  
Prediction Error ◽  
Diameter Ratio ◽  
Nonlinear Regression Model ◽  
Average Prediction Error ◽  
Model Average ◽  
Dimension Analysis ◽  
Longmaxi Shale

The compressive strength of shale is a comprehensive index for evaluating the shale strength, which is linked to shale well borehole stability. Based on correlation analysis between factors (confining stress, height/diameter ratio, bedding angle, and porosity) and shale compressive strength (Longmaxi Shale in Sichuan Basin, China), we develop a dimension analysis-based model for prediction of shale compressive strength. A nonlinear-regression model is used for comparison. A multitraining method is used to achieve reliability of model prediction. The results show that, compared to a multi-nonlinear-regression model (average prediction error = 19.5%), the average prediction error of the dimension analysis-based model is 19.2%. More importantly, our dimension analysis-based model needs to determine only one parameter, whereas the multi-nonlinear-regression model needs to determine five. In addition, sensitivity analysis shows that height/diameter ratio has greater sensitivity to compressive strength than other factors.

scholarly journals A Monograph on Nonlinear Regression Models

2018 ◽  
Vol 7 (4.10) ◽  
pp. 543
Author(s):  
B. Mahaboob ◽  
B. Venkateswarlu ◽  
C. Narayana ◽  
J. Ravi sankar ◽  
P. Balasiddamuni
Keyword(s):  
Regression Model ◽  
Least Squares ◽  
Nonlinear Regression ◽  
Nonlinear Models ◽  
Nonlinear Least Squares ◽  
Error Variance ◽  
Ordinary Least Squares ◽  
Parameter Estimates ◽  
Nonlinear Regression Model ◽  
Nonlinear Regression Models

This research article uses Matrix Calculus techniques to study least squares application of nonlinear regression model, sampling distributions of nonlinear least squares estimators of regression parametric vector and error variance and testing of general nonlinear hypothesis on parameters of nonlinear regression model. Arthipova Irina et.al [1], in this paper, discussed some examples of different nonlinear models and the application of OLS (Ordinary Least Squares). MA Tabati et.al (2), proposed a robust alternative technique to OLS nonlinear regression method which provide accurate parameter estimates when outliers and/or influential observations are present. Xu Zheng et.al [3] presented new parametric tests for heteroscedasticity in nonlinear and nonparametric models.  

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