# Robustness against conflicting prior information in regression

@inproceedings{Gagnon2021RobustnessAC, title={Robustness against conflicting prior information in regression}, author={Philippe Gagnon}, year={2021} }

Including prior information about model parameters is a fundamental step of any Bayesian statistical analysis. It is viewed positively by some as it allows, among others, to quantitatively incorporate expert opinion about model parameters. It is viewed negatively by others because it sets the stage for subjectivity in statistical analysis. Certainly, it creates problems when the inference is skewed due to a conflict with the data collected. According to the theory of conflict resolution (O…

## References

SHOWING 1-10 OF 21 REFERENCES

A New Bayesian Approach to Robustness Against Outliers in Linear Regression

- Mathematics, Computer Science
- 2016

This paper proposes a model with super heavy-tailed errors, and proves that it is wholly robust, meaning that the impact of outliers gradually vanishes as they move further and further away form the general trend.

Robust Bayesian Regression Analysis Using Ramsay-Novick Distributed Errors with Student-t Prior

- Mathematics
- 2018

This paper investigates bayesian treatment of regression modelling with Ramsay - Novick (RN) distribution specifically developed for robust inferential procedures. It falls into the category of…

Bayesian heavy-tailed models and conflict resolution: A review

- Mathematics
- 2012

We review a substantial literature, spanning 50 years, concerning the resolution of conicts using Bayesian heavy-tailed models. Conicts arise when di¤erent sources of information about the model…

Bayesian robustness to outliers in linear regression and ratio estimation

- MathematicsBrazilian Journal of Probability and Statistics
- 2019

Whole robustness is a nice property to have for statistical models. It implies that the impact of outliers gradually decreases to nothing as they converge towards plus or minus infinity. So far, the…

An automatic robust Bayesian approach to principal component regression

- Computer Science, MathematicsJournal of Applied Statistics
- 2020

A Bayesian approach that is robust to outliers in both the dependent variable and the covariates is introduced, compared to its nonrobust Bayesian counterpart, the traditional frequentist approach and a commonly employed robust frequentist method.

Bayesian Model Averaging for Linear Regression Models

- Mathematics
- 1997

Abstract We consider the problem of accounting for model uncertainty in linear regression models. Conditioning on a single selected model ignores model uncertainty, and thus leads to the…

The Bayesian Lasso

- Mathematics
- 2008

The Lasso estimate for linear regression parameters can be interpreted as a Bayesian posterior mode estimate when the regression parameters have independent Laplace (i.e., double-exponential) priors.…

The Choice of Variables in Multiple Regression

- Mathematics
- 1968

Professor R. L. PLACKETT in the Chair] SUMMARY This paper is concerned with the analysis of data from a multiple regression of a single variable, y, on a set of independent variables, xl, x2, .. .…

On Outlier Rejection Phenomena in Bayes Inference

- Mathematics
- 1979

SUMMARY Inference is considered for a location parameter given a random sample. Outliers are not explicitly modelled, but rejection of extreme observations occurs naturally in any Bayesian analysis…

Robustness to outliers in location–scale parameter model using log-regularly varying distributions

- Mathematics
- 2015

Estimating the location and scale parameters is common in statistics, using, for instance, the well-known sample mean and standard deviation. However, inference can be contaminated by the presence of…