
doi: 10.1007/bf02296650
Let each of several (generally interdependent) random vectors, taken separately, be influenced by a particular set of external factors. Under what kind of the joint dependence of these vectors on the union of these factor sets can one say that each vector is selectively influenced by “its own” factor set? The answer proposed and elaborated in this paper is: One can say this if and only if one can find a factor-independent random vector given whose value the vectors in question are conditionally independent, with their conditional distributions selectively influenced by the corresponding factor sets. Equivalently, the random vectors should be representable as deterministic functions of “their” factor sets and of some mutually independent and factor-independent random variables, some of which may be shared by several of the functions.
selective influence, conditional independence, Thurstonian modeling, processing architectures, Applications of statistics to psychology, marginal selectivity
selective influence, conditional independence, Thurstonian modeling, processing architectures, Applications of statistics to psychology, marginal selectivity
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