Spatial Effects in Dyadic Data

Spatial Effects in Dyadic Data

Political units often spatially depend in their political choices on other units. This is no less so in dyadic settings where, as in much of international relations research, the focus of the analysis is the pair or dyad of two political units. Yet, with few exceptions social scientists have analyzed contagion only in monadic datasets, consisting of individual political units. This article expl...

Bilinear Mixed Effects Models for Dyadic Data

This article discusses the use of a symmetric multiplicative interaction effect to capture certain types of third-order dependence patterns often present in social networks and other dyadic datasets. Such an effect, along with standard linear fixed and random effects, is incorporated into a generalized linear model, and a Markov chain Monte Carlo algorithm is provided for Bayesian estimation an...

Estimating Peer Effects in Longitudinal Dyadic Data Using Instrumental Variables

The identification of causal peer effects (also known as social contagion or induction) from observational data in social networks is challenged by two distinct sources of bias: latent homophily and unobserved confounding. In this paper, we investigate how causal peer effects of traits and behaviors can be identified using genes (or other structurally isomorphic variables) as instrumental varia...

Learning from Dyadic Data

Dyadic data refers to a domain with two nite sets of objects in which observations are made for dyads, i.e., pairs with one element from either set. This type of data arises naturally in many application ranging from computational linguistics and information retrieval to preference analysis and computer vision. In this paper, we present a systematic, domain-independent framework of learning fro...

Bayesian Prediction in Spatial Data Analysis