Preliminaries. Prior-Free Probabilistic Inference. Two Fundamental Principles. Inferential Models. Predictive Random Sets. Conditional Inferential Models. Marginal Inferential Models. Normal Linear Models. Prediction of Future Observations. Simultaneous Inference on Multiple Assertions. Generalized Inferential Models. Future Research Topics. Bibliography. Index.
Ryan Martin is an associate professor in the Department of Mathematics, Statistics, and Computer Science at the University of Illinois at Chicago.
Chuanhai Liu is a professor in the Department of Statistics at Purdue University.
"The book . . . delivers on its promise. It should be read by all
statisticians with an interest in the foundations and development
of the statistical methods for inference."
~Michael J. Lew, University of Melbourne" . . . the book covers the
motivations for the IM framework, the basic theory behind its
calibration properties, a number of its applications and gives a
new way of thinking compared to existing schools of thought on
statistical inference"
~Apostolos Batsidis (Ioannina), Zentralblatt MATH
"The book . . . delivers on its promise. It should be read by all
statisticians with an interest in the foundations and development
of the statistical methods for inference."
~Michael J. Lew, University of Melbourne" . . . the book covers the
motivations for the IM framework, the basic theory behind its
calibration properties, a number of its applications and gives a
new way of thinking compared to existing schools of thought on
statistical inference"
~Apostolos Batsidis (Ioannina), Zentralblatt MATH
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