Modeling Heterogeneous Peer Assortment Effects using Latent Class Pseudo-Maximum Likelihood Exponential Random Graph Models

This thesis develops a class of models for inference on networks called Sender/Receiver Latent Class Exponential Random Graph Models (SRLCERGMs). This class of models extends the existing Exponential Random Graph Modeling framework to allow analysts to model unobserved heterogeneity in the effects of nodal covariates and network features. Simulations across a variety of conditions are presented to evaluate the performance of this technique, and an empirical example regarding substance use among adolescents is also presented. Implications for the analysis of social networks in psychological science are discussed.Master of Art

Statistical Methodologies

Statistical practices have recently been questioned by numerous independent authors, to the extent that a significant fraction of accepted research findings can be questioned. This suggests that statistical methodologies may have gone too far into an engineering practice, with minimal concern for their foundation, interpretation, assumptions, and limitations, which may be jeopardized in the current context. Disguised by overwhelming data sets, advanced processing, and stunning presentations, the basic approach is often intractable to anyone but the analyst. The hierarchical nature of statistical inference, exemplified by Bayesian aggregation of prior and derived knowledge, may also be challenging. Conceptual simplified studies of the kind presented in this book could therefore provide valuable guidance when developing statistical methodologies, but also applying state of the art with greater confidence

The analysis of social network data: an exciting frontier for statisticians

The catalyst for this paper is the recent interest in the relationship between social networks and an individual's health, which has arisen following a series of papers by Nicholas Christakis and James Fowler on person- to-person spread of health behaviors. In this issue, they provide a detailed explanation of their methods that offers insights, justifications, and responses to criticisms [1]. In this paper, we introduce some of the key statistical methods used in social network analysis and indicate where those used by Christakis and Fowler (CF) fit into the general framework. The intent is to provide the background necessary for readers to be able to make their own evaluation of the work by CF and understand the challenges of research involving social networks. We entertain possible solutions to some of the difficulties encountered in accounting for confounding effects in analyses of peer effects and provide comments on the contributions of CF

ISBIS 2016: Meeting on Statistics in Business and Industry

This Book includes the abstracts of the talks presented at the 2016 International Symposium on Business and Industrial Statistics, held at Barcelona, June 8-10, 2016, hosted at the Universitat Politècnica de Catalunya - Barcelona TECH, by the Department of Statistics and Operations Research. The location of the meeting was at ETSEIB Building (Escola Tecnica Superior d'Enginyeria Industrial) at Avda Diagonal 647. The meeting organizers celebrated the continued success of ISBIS and ENBIS society, and the meeting draw together the international community of statisticians, both academics and industry professionals, who share the goal of making statistics the foundation for decision making in business and related applications. The Scientific Program Committee was constituted by: David Banks, Duke University Amílcar Oliveira, DCeT - Universidade Aberta and CEAUL Teresa A. Oliveira, DCeT - Universidade Aberta and CEAUL Nalini Ravishankar, University of Connecticut Xavier Tort Martorell, Universitat Politécnica de Catalunya, Barcelona TECH Martina Vandebroek, KU Leuven Vincenzo Esposito Vinzi, ESSEC Business Schoo