Some Comments on Latent Structure Analysis, Arising Out of a Paper by R.D. Gill

The model of latent structure analysis for k discrete-valued variates was explained and illustrated by R.D. G ill [5], Here the latent structure model is related to a condition for a covariance matrix, as in factor analysis theory, and a word of warning is given for the interpretation of a factor analysis from discrete valued variables.     

On the choice of support of Re-descendingψ-Functions in linear models with asymmetric error distributions

InM-estimation of the regression parameter vector in the linear model, we discuss the choice of the support of certain re-descendingψ-functions for both cases when the distribution of the i.i.d. errors is partially known and when it is completely functionally unknown. Research supported in part by the Natural Sciences and Engineering Research Council of Canada.     

In search of efficient network structures: the needle in the haystack

Searching for efficient networks can prove a very difficult analytical and even computational task. In this paper, we explore the possibility of using the genetic algorithms (GA) technique to identify efficient network structures in the case of non-trivial payoff functions. The robustness of this method in predicting optimal networks is tested on the two simple stylized models introduced by Jackson and Wolinsky (1996), for which the efficient networks are known over the whole state space of the parameters’ values. This approach allows us to obtain new exploratory results in the case of the linear-spatialized connections model proposed by Johnson and Gilles (Rev Econ Des 5:273–299, 2000), for which the efficient allocation of bilateral connections is driven by contradictory forces that push either for a centralized structure around a coordinating agent, or for only locally and evenly distributed connections. Murat Yıldızoğlu gratefully acknowledges the support of the CCRDT program of Aquitaine Region.