A trio of inference problems that could win you a Nobel Prize in statistics (if you help fund it)

Statistical inference is a field full of problems whose solutions require the same intellectual force needed to win a Nobel Prize in other scientific fields. Multiresolution inference is the oldest of the trio. But emerging applications such as individualized medicine have challenged us to the limit...

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Bibliographic Details
Published inPast, Present, and Future of Statistical Science pp. 561 - 586
Format Book Chapter
LanguageEnglish
Published Chapman and Hall/CRC 2014
Subjects
Analyst’s Model
Multiresolution Framework
Common Logics
Defect Index
Resolution Level
MI Estimator
Lasso
Bias Reduction Techniques
MLE
Vice Versa
Individualized Medicine
Administrative Record
Observed Data Yobs
Imputer’s Model
National Academies
Horvitz Thompson Estimator
Inference Problems
Eve
Statistical Inference
LEHD
Inference Purposes
Mi
Small MSE
Qualitative Consistency
Online AccessGet full text
DOI10.1201/b16720-52

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Summary:Statistical inference is a field full of problems whose solutions require the same intellectual force needed to win a Nobel Prize in other scientific fields. Multiresolution inference is the oldest of the trio. But emerging applications such as individualized medicine have challenged us to the limit: infer estimands with resolution levels that far exceed those of any feasible estimator. Multi-phase inference is another reality because (big) data are almost never collected, processed, and analyzed in a single phase. The newest of the trio is multisource inference, which aims to extract information in data coming from very different sources, some of which were never intended for inference purposes. All of these challenges call for an expanded paradigm with greater emphases on qualitative consistency and relative optimality than do our current inference paradigms.
DOI:10.1201/b16720-52