Confidence Intervals for Soil Properties within Map Units

2015 ◽  
pp. 213-229 ◽  
Author(s):  
Fred J. Young ◽  
J. M. Maatta ◽  
R. David Hammer
Keyword(s):  
Soil Properties ◽  
Confidence Intervals

scholarly journals Soybean yield modeling using bootstrap methods for small samples

2016 ◽  
Vol 14 (3) ◽  
pp. e0207 ◽  
Author(s):  
Gustavo H. Dalposso ◽  
Miguel A. Uribe-Opazo ◽  
Jerry A. Johann
Keyword(s):  
Regression Model ◽  
Soil Properties ◽  
Confidence Intervals ◽  
Great Influence ◽  
Multiple Linear Regression Model ◽  
Model Parameters ◽  
Bootstrap Methods ◽  
Soybean Yield ◽  
Chemical Soil ◽  
Physical And Chemical

One of the problems that occur when working with regression models is regarding the sample size; once the statistical methods used in inferential analyzes are asymptotic if the sample is small the analysis may be compromised because the estimates will be biased. An alternative is to use the bootstrap methodology, which in its non-parametric version does not need to guess or know the probability distribution that generated the original sample. In this work we used a set of soybean yield data and physical and chemical soil properties formed with fewer samples to determine a multiple linear regression model. Bootstrap methods were used for variable selection, identification of influential points and for determination of confidence intervals of the model parameters. The results showed that the bootstrap methods enabled us to select the physical and chemical soil properties, which were significant in the construction of the soybean yield regression model, construct the confidence intervals of the parameters and identify the points that had great influence on the estimated parameters.

A problem with confidence intervals.

1995 ◽  
Vol 50 (12) ◽  
pp. 1102-1103 ◽  
Author(s):  
Robert W. Frick
Keyword(s):  
Confidence Intervals

A Note on Confidence Intervals and Model Specification

Marketing ZFP ◽  
2019 ◽  
Vol 41 (4) ◽  
pp. 33-42
Author(s):  
Thomas Otter
Keyword(s):  
Confidence Intervals ◽  
Generalized Linear Model ◽  
Statistical Information ◽  
Regression Coefficients ◽  
Model Specification ◽  
Model Parameters ◽  
Exploratory Research ◽  
Empirical Calibration ◽  
Credible Intervals ◽  
Model Structures

Empirical research in marketing often is, at least in parts, exploratory. The goal of exploratory research, by definition, extends beyond the empirical calibration of parameters in well established models and includes the empirical assessment of different model specifications. In this context researchers often rely on the statistical information about parameters in a given model to learn about likely model structures. An example is the search for the 'true' set of covariates in a regression model based on confidence intervals of regression coefficients. The purpose of this paper is to illustrate and compare different measures of statistical information about model parameters in the context of a generalized linear model: classical confidence intervals, bootstrapped confidence intervals, and Bayesian posterior credible intervals from a model that adapts its dimensionality as a function of the information in the data. I find that inference from the adaptive Bayesian model dominates that based on classical and bootstrapped intervals in a given model.

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