Classical confidence intervals and Bayesian probability estimates for ends of local taxon ranges

1989 ◽  
Vol 21 (4) ◽  
pp. 411-427 ◽  
Author(s):  
David Strauss ◽  
Peter M. Sadler
Keyword(s):  
Confidence Intervals ◽  
Bayesian Probability ◽  
Probability Estimates

scholarly journals Bayesian probability estimates are not necessary to make choices satisfying Bayes’ rule in elementary situations

2015 ◽  
Vol 6 ◽  
Author(s):  
Artur Domurat ◽  
Olga Kowalczuk ◽  
Katarzyna Idzikowska ◽  
Zuzanna Borzymowska ◽  
Marta Nowak-Przygodzka
Keyword(s):  
Bayesian Probability ◽  
Bayes Rule ◽  
Probability Estimates

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.

Development of Unit Commitment Model Considering Confidence Intervals of Photovoltaics Forecast and Analysis of a Large Scale Power System

2016 ◽  
Vol 136 (5) ◽  
pp. 484-496 ◽  
Author(s):  
Yusuke Udagawa ◽  
Kazuhiko Ogimoto ◽  
Takashi Oozeki ◽  
Hideaki Ohtake ◽  
Takashi Ikegami ◽  
...  
Keyword(s):  
Power System ◽  
Confidence Intervals ◽  
Large Scale ◽  
Unit Commitment
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