
Fiducial Symmetry in Action
Symmetry is key in classical and modern physics. A striking example is c...
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Statistical reform and the replication crisis
The replication crisis has prompted many to call for statistical reform ...
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Statistical inference for Axiom A attractors
From the climate system to the effect of the internet on society, chaoti...
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VB and R codes using Households databases available in the NSI's : A prelude to statistical applied studies
We describe the main features of the households databases we can find in...
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Statistical inference using SGD
We present a novel method for frequentist statistical inference in Mest...
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The basic principles and the structure and algorithmically software of computing by hypercomplex number
In article the basic principles put in a basis of algorithmicallysoftwar...
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Basic stochastic transmission models and their inference
The current survey paper concerns stochastic mathematical models for the...
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The Scaled Uniform Model Revisited
Sufficiency, Conditionality and Invariance are basic principles of statistical inference. Current mathematical statistics courses do not devote much teaching time to these classical principles, and even ignore the latter two, in order to teach modern methods. However, being the philosophical cornerstones of statistical inference, a minimal understanding of these principles should be part of any curriculum in statistics. The scaled uniform model is used here to demonstrate the importance and usefulness of the principles. The main focus is on the conditionality principle that is probably the most basic and less familiar among the three.
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