Kavli Affiliate: Atsushi Yagishita
| First 5 Authors: Kentaro Tanaka, Atsushi Yagishita, Masami Miyakawa, ,
| Summary:
For a given statistical model, it often happens that it is necessary to
intervene the model to reduce the variances of the output variables. In
structural equation models, this can be done by changing the values of the path
coefficients by intervention. First, we explain that the expectations and
variance matrix can be decomposed into several parts in terms of the total
effects. Then, we show that an algorithm to obtain intervention method which
minimizes the weighted sum of the variances can be formulated as a convex
quadratic programming. This formulation allows us to impose boundary conditions
for the intervention, so that we can find the practical solutions. We also
treat a problem to adjust the expectations on targets.
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