How do we choose our default methods?

The field of statistics continues to be divided into competing schools of thought. In theory one might imagine choosing the uniquely best method for each problem as it arises, but in practice we choose for ourselves (and recommend to others) default principles, models, and methods to be used in a wi...

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Bibliographic Details
Published inPast, Present, and Future of Statistical Science pp. 317 - 326
Format Book Chapter
LanguageEnglish
Published Chapman and Hall/CRC 2014
Subjects
MCMC
Pluralist’s Dilemma
Uncheckable
Bayesian Methods
Full Bayesian Approach
Agnostic
Default Principles
Machine Learning Algorithms
Posterior Predictive Checks
L1 Regularization
Boston Housing Data
Partial Pooling
Toy Problems
Smoothed
Bayesian Data Analysis
Strong Prior Knowledge
Benchmark Problems
Good Statistical Properties
Prior Distribution
Default Methods
Violate
Timeline
Unlimited
Informal Criteria
Hold
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DOI10.1201/b16720-33

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Summary:The field of statistics continues to be divided into competing schools of thought. In theory one might imagine choosing the uniquely best method for each problem as it arises, but in practice we choose for ourselves (and recommend to others) default principles, models, and methods to be used in a wide variety of settings. This chapter briefly considers the informal criteria we use to decide what methods to use and what principles to apply in statistics problems.
DOI:10.1201/b16720-33