Sonet-1: Social network analysis and modeling system

SONET-I is a preliminary but completely operational version of a package of computer programs for the analysis of social structure. The programs were written to implement a strategy for the analysis of social networks which the authors have developed over the past several years. The conceptual basis for the research method has its origins in graph theory on the one hand and in anthropological studies of social organization — especially kinship studies — on the other. The method can, in fact, be seen as a graph theoretic formalization and generalization of kinship-based methods of social analysis often associated with British social anthropology.     

Muscle-power quality: does sex or race affect movement velocity in older adults?

To determine sex and race differences in muscle power per unit of muscle contraction, knee-extensor muscle power normalized for knee-extensor muscle volume was measured in 79 middle-aged and older adults (30 men and 49 women, age range 50-85 years). Results revealed that women displayed a 38% faster peak movement velocity than men and African Americans had a 14% lower peak movement velocity than Whites of a similar age when expressed per unit of involved muscle (p < .001). As expected, men exhibited greater knee-extensor strength and peak power per unit of muscle than women, but women had a faster knee- extension movement velocity per unit of muscle than men at the same relative strength level. Moreover, African Americans had greater knee-extensor muscle volume than Whites but exhibited lower knee-extensor strength and lower movement velocity per unit of muscle when tested at the same relative strength levels.     

Outsourcing to a Powerful Contract Manufacturer: The Effect of Learning‐by‐Doing

The contract manufacturing industry has grown rapidly in recent years as firms have increasingly outsourced production to reduce costs. This growth has created powerful contract manufacturers (CMs) in several industries. Achieving a competitive cost position is often a primary motive for outsourcing. Outsourcing influences both the original equipment manufacturer's (OEM) and the CM's production levels, and, therefore, through learning‐by‐doing renders future costs dependent on past outsourcing decisions. As such, outsourcing should not be viewed as a static decision that, once made, is not revisited. We address these considerations by analyzing a two‐period game between an OEM and a powerful CM wherein both firms can reduce their production costs through learning‐by‐doing. We find that partial outsourcing, wherein the OEM simultaneously outsources and produces in‐house, can be an optimal strategy. Also, we find that the OEM's outsourcing strategy may be dynamic—i.e., change from period to period. In addition, we find both that the OEM may engage in production for leverage (i.e., produce internally when at a cost disadvantage) and that the CM may engage in low balling. These and other findings in this paper demonstrate the importance of considering learning, the power of the CM, and future periods when making outsourcing decisions.     

A Compressive Sensing Based Analysis of Anomalies in Generalized Linear Models

In this article, we present a compressive sensing based framework for generalized linear model regression that employs a two-component noise model and convex optimization techniques to simultaneously detect outliers and determine optimally sparse representations of noisy data from arbitrary sets of basis functions. We then extend our model to include model order reduction capabilities that can uncover inherent sparsity in regression coefficients and achieve simple, superior fits. Second, we use the mixed ?₂/?₁ norm to develop another model that can efficiently uncover block-sparsity in regression coefficients. By performing model order reduction over all independent variables and basis functions, our algorithms successfully deemphasize the effect of independent variables that become uncorrelated with dependent variables. This desirable property has various applications in real-time anomaly detection, such as faulty sensor detection and sensor jamming in wireless sensor networks. After developing our framework and inheriting a stable recovery theorem from compressive sensing theory, we present two simulation studies on sparse or block-sparse problems that demonstrate the superior performance of our algorithms with respect to (1) classic outlier-invariant regression techniques like least absolute value and iteratively reweighted least-squares and (2) classic sparse-regularized regression techniques like LASSO.     

Geometric characterization of planar regression

The geometric characterization of linear regression in terms of the ‘concentration ellipse’ by Galton [Galton, F., 1886, Family likeness in stature (with Appendix by Dickson, J.D.H.). Proceedings of the Royal Society of London, 40, 42–73.] and Pearson [Pearson, K., 1901, On lines and planes of closest fit to systems of points in space. Philosophical Magazine, 2, 559–572.] was extended to the case of unequal variances of the presumably uncorrelated errors in the experimental data [McCartin, B.J., 2003, A geometric characterization of linear regression. Statistics, 37(2), 101–117.]. In this paper, this geometric characterization is further extended to planar (and also linear) regression in three dimensions where a beautiful interpretation in terms of the concentration ellipsoid is developed.     

An Empirical Study of Statistical Properties of Variance Partition Coefficients for Multi-Level Logistic Regression Models

Partitioning the variance of a response by design levels is challenging for binomial and other discrete outcomes. Goldstein (2003 Goldstein , H. ( 2003 ). Multilevel Statistical Models. 3rd ed . London : Edward Arnold . [Google Scholar]) proposed four definitions for variance partitioning coefficients (VPC) under a two-level logistic regression model. In this study, we explicitly derived formulae for multi-level logistic regression model and subsequently studied the distributional properties of the calculated VPCs. Using simulations and a vegetation dataset, we demonstrated associations between different VPC definitions, the importance of methods for estimating VPCs (by comparing VPC obtained using Laplace and penalized quasilikehood methods), and bivariate dependence between VPCs calculated at different levels. Such an empirical study lends an immediate support to wider applications of VPC in scientific data analysis.