Conditional inferential models: combining information for prior‐free probabilistic inference

The inferential model (IM) framework provides valid prior‐free probabilistic inference by focusing on predicting unobserved auxiliary variables. But, efficient IM‐based inference can be challenging when the auxiliary variable is of higher dimension than the parameter. Here we show that features of t...

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Veröffentlicht in:	Journal of the Royal Statistical Society. Series B, Statistical methodology Statistical methodology, 2015-01, Vol.77 (1), p.195-217
Hauptverfasser:	Martin, Ryan, Liu, Chuanhai
Format:	Artikel
Sprache:	eng
Schlagworte:	Agglomeration Auxiliary variable Bayes Bayesian analysis Bayesian method Bayesian theory Belief function Casts Conditional probabilities Conditioning Differential equation Differential equations Dimensionality reduction Distribution functions Economic models Forecasts Inference Mathematical expressions Mathematical functions Mathematical models prediction Predictive random set Probabilistic inference Probabilistic modeling Probability Sampling distributions Statistical inference Statistical variance Statistics Strategy Studies Sufficiency Validity Variance
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