Pole sensitivity in coupling networks

When designing lossless networks, one is faced with the problem of choosing a zero configuration for the reflection coefficient ?(s). Bode showed that if parasitic capacitances are present as well as for power transfer optimization, the best zero configuration is to locate all of them in the left half-plane. Recently, Bode's result was generalized to include parasitic immitances at both ends (source and load) of the quadripole.This work adds a new result to the theory by looking at the overall transfer function poles. It is shown that (a) if the source resistance varies, one should select left half-plane zeros for ?(s); and (b) if the load impedance varies, one should select right half-plane zeros.The pole sensitivity criterion used is the distance between the actual transfer function poles and the poles of the ideal transfer function.     

A Two-Stage Approach to Synthesizing Covariance Matrices in Meta-Analytic Structural Equation Modeling

A great obstacle for wider use of structural equation modeling (SEM) has been the difficulty in handling categorical variables. Two data sets with known structure between 2 related binary outcomes and 4 independent binary variables were generated. Four SEM strategies and resulting apparent validity were tested: robust maximum likelihood (ML), tetrachoric correlation matrix input followed by SEM ML analysis, SEM ML estimation for the sum of squares and cross-products (SSCP) matrix input obtained by the log-linear model that treated all variables as dependent, and asymptotic distribution-free (ADF) SEM estimation. SEM based on the SSCP matrix obtained by the log-linear model and SEM using robust ML estimation correctly identified the structural relation between the variables. SEM using ADF added an extra parameter. SEM based on tetrachoric correlation input did not specify the data generating process correctly. Apparent validity was similar for all models presented. Data transformation used in log-linear modeling can serve as an input for SEM.