Titlebook: Unobserved Variables; Models and Misunders David J. Bartholomew Book 2013 The Author(s) 2013 Bayesian methods.Latent variables.Missing data

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Identifiability,be that several models explain the data equally well. When this happens there is said to be a lack of identifiability. Failure to take full account of this fact, especially in the social sciences, has led to many over-confident claims about the nature of social reality. Lack of identifiability withi

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Categorical Variables,sis of latter takes needs to be spelt out. This is done in the present chapter where categorical variables appear in the standard data matrix. The key step is the replacement of a continuous variable by an indicator vector showing into which of a number of categories a sample member falls. In some c

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Models for Time Series,hus reasonably begin with the joint distribution of all variables—past and future—from which the relevant conditional distribution may be determined. Two types of model will be described. The first specifies the mean values of the joint distribution and the second, its covariance structure. The form

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Bayesian and Computational Methods,to bring the whole of statistical inference within the purview of this Brief. In a formal sense, at least, this is the case. However, it is important to distinguish the different kinds of uncertainty with which we are dealing. First, there are the actual, observable, distributions of the manifest va