A mixed effects model for longitudinal relational and network data, with applications to international trade and conflict

The focus of this paper is an approach to the modeling of longitudinal social network or relational data. Such data arise from measurements on pairs of objects or actors made at regular temporal intervals, resulting in a social network for each point in time. In this article we represent the network and temporal dependencies with a random effects model, resulting in a stochastic process defined by a set of stationary covariance matrices. Our approach builds upon the social relations models of Warner, Kenny and Stoto [Journal of Personality and Social Psychology 37 (1979) 1742--1757] and Gill and Swartz [Canad. J. Statist. 29 (2001) 321--331] and allows for an intra- and inter-temporal representation of network structures. We apply the methodology to two longitudinal data sets: international trade (continuous response) and militarized interstate disputes (binary response).Comment: Published in at http://dx.doi.org/10.1214/10-AOAS403 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Social Network Analysis on Food Web and Dispute Data

Several social science disciplines, especially anthropology and sociology, have long engaged in social network analyses. Social Network Analysis (SNA) uses network theory to analyse social networks – a network that often involves individual social actors (people) and relations between them. Social network analysis aims at understanding the network structure by description, visualization, and statistical modeling. In this research, the illustration of the use of SNA is done on two different datasets: food web data and militarized interstate dispute data

The background of the McIntire-Stennis Act for Cooperative Forestry Research

Cover title.Includes bibliographical references (pages [14-15])

Analysis of Morris Water Maze data with Bayesian statistical methods

Neuroscientists commonly use a Morris Water Maze to assess learning in rodents. In his kind of a maze, the subjects learn to swim toward a platform hidden in opaque water as they orient themselves according to the cues on the walls. This protocol presents a challenge to statistical analysis, because an artificial cut-off must be set for those experimental subjects that do not reach the platform so as they do not drown from exhaustion. This fact leads to the data being right censored. In our experimental data, which compares learning in rodents that have chemically induced symptoms of schizophrenia to a control group of rodents a cut-off of 60 seconds was used, and is the mode of the distribution. Utilizing Bayesian inferential procedures, we account for the censoring in the data and compare the results of learning between the treatment and control group

A Statistical Social Network Model for Consumption Data in Food Webs

We adapt existing statistical modeling techniques for social networks to study consumption data observed in trophic food webs. These data describe the feeding volume (non-negative) among organisms grouped into nodes, called trophic species, that form the food web. Model complexity arises due to the extensive amount of zeros in the data, as each node in the web is predator/prey to only a small number of other trophic species. Many of the zeros are regarded as structural (non-random) in the context of feeding behavior. The presence of basal prey and top predator nodes (those who never consume and those who are never consumed, with probability 1) creates additional complexity to the statistical modeling. We develop a special statistical social network model to account for such network features. The model is applied to two empirical food webs; focus is on the web for which the population size of seals is of concern to various commercial fisheries.Comment: On 2013-09-05, a revised version entitled "A Statistical Social Network Model for Consumption Data in Trophic Food Webs" was accepted for publication in the upcoming Special Issue "Statistical Methods for Ecology" in the journal Statistical Methodolog

Modeling Foreign Direct Investment as a Longitudinal Social Network

An extensive literature in international and comparative political economy has focused on the how the mobility of capital affects the ability of governments to tax and regulate firms. The conventional wisdom holds that governments are in competition with each other to attract foreign direct investment (FDI). Nation-states observe the fiscal and regulatory decisions of competitor governments, and are forced to either respond with policy changes or risk losing foreign direct investment, along with the politically salient jobs that come with these investments. The political economy of FDI suggests a network of investments with complicated dependencies. We propose an empirical strategy for modeling investment patterns in 24 advanced industrialized countries from 1985-2000. Using bilateral FDI flow and stock data, we examine the nature of the networks in relation to a set of covariates - in particular differences in tax rates between pairs of nations. Our statistical model is based on the methodology developed by Hoff (2005), Westveld (2007), Westveld and Hoff (2009b). The model allows the temporal examination of each nation\u27s activity level in investing and attractiveness to investors. Additionally, the model considers the temporal examination of reciprocity between pairs of nations, as well as the notion of clusterability. For both the flow and stock data, there exist a data set based on reports from senders (out-reported-data) and a data set based on reports from receivers (in-reported-data). We extend the model by treating these two data sets as independent replicates (for the flow and stock data separately), conditional on a mean parameter representing an underlying value of FDI, along with random effects within the variance portion of the distribution of the response that allows for discrepancy between the two data points (in and out data). Using a fully Bayesian approach, we also impute the missing data within a MCMC algorithm used to fit the model

Latent Causal Socioeconomic Health Index

This research develops a model-based LAtent Causal Socioeconomic Health (LACSH) index at the national level. We build upon the latent health factor index (LHFI) approach that has been used to assess the unobservable ecological/ecosystem health. This framework integratively models the relationship between metrics, the latent health, and the covariates that drive the notion of health. In this paper, the LHFI structure is integrated with spatial modeling and statistical causal modeling, so as to evaluate the impact of a continuous policy variable (mandatory maternity leave days and government's expenditure on healthcare, respectively) on a nation's socioeconomic health, while formally accounting for spatial dependency among the nations. A novel visualization technique for evaluating covariate balance is also introduced for the case of a continuous policy (treatment) variable. We apply our LACSH model to countries around the world using data on various metrics and potential covariates pertaining to different aspects of societal health. The approach is structured in a Bayesian hierarchical framework and results are obtained by Markov chain Monte Carlo techniques.Comment: 31 pages. arXiv admin note: substantial text overlap with arXiv:1911.0051