A computer tool for a minimax criterion in binary response and heteroscedastic simple linear regression models

2017 ◽  
Vol 138 ◽  
pp. 105-115
Author(s):  
V. Casero-Alonso ◽  
J. López-Fidalgo ◽  
B. Torsney
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Binary Response ◽  
Simple Linear Regression ◽  
Linear Regression Models ◽  
Computer Tool ◽  
Minimax Criterion

On Consistency of Estimators in Simple Linear Regression Models

2002 ◽  
Vol 53 (3-4) ◽  
pp. 261-264 ◽  
Author(s):  
Anindya Roy ◽  
Thomas I. Seidman
Keyword(s):  
Linear Regression ◽  
Least Squares ◽  
Regression Models ◽  
Sufficient Condition ◽  
Simple Linear Regression ◽  
Linear Regression Models ◽  
Least Squares Estimators

We derive a property of real sequences which can be used to provide a natural sufficient condition for the consistency of the least squares estimators of slope and intercept for a simple linear regression models.

scholarly journals Regression models for productivity prediction in cactus pear cv. Gigante

2020 ◽  
Vol 24 (11) ◽  
pp. 721-727
Author(s):  
Bruno V. C. Guimarães ◽  
Sérgio L. R. Donato ◽  
Ignacio Aspiazú ◽  
Alcinei M. Azevedo ◽  
Abner J. de Carvalho
Keyword(s):  
Linear Regression ◽  
Multiple Regression ◽  
Regression Models ◽  
Yield Prediction ◽  
Simple Linear Regression ◽  
Linear Regression Models ◽  
Ct Number ◽  
Cactus Pear ◽  
Quadratic Interaction ◽  
Multiple Regression Models

ABSTRACT The understanding of plant behavior and its reflexes on yield is essential for rural planning; thus, the biomathematical models are promising in the yield prediction of cactus pear cv. Gigante. This study aimed to adjust, through simple and multiple regression analysis, models for predicting the yield of cactus pear cv. Gigante. The study, using homogeneous treatments, was developed at the Instituto Federal Baiano, Campus of Guanambi, Bahia, Brazil. Data were collected in an area consisting of 384 basic units (plants), in which the yield, defined as a dependent variable, and the predictor variables: plant height (PH), cladode length (CL), cladode width (CW), and cladode thickness (CT), number of cladodes (NC), cladode area (CA), and total cladode area (TCA) were evaluated. Simple linear regression models, multiple regression models only with simple effects for the explanatory variables, and the multiple regression models considering the simple and quadratic effects, and all its possible interactions were adjusted. From this last model, a reduced model was obtained by discarding the less relevant effects, using the Stepwise methodology. The use of the vegetative traits, TCA, NC, CA, CL, CT, and CW, through the adoption of multiple linear regression, quadratic interaction or just the variable TCA by the use of simple linear regression, allows the yield prediction of cactus pear, with adjusted R² of 0.82, 0.76, and 0.74, respectively.

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