Peter D Hoff
Biographic Data
| ID | 126944 |
|---|---|
| NAME | Peter D Hoff |
| GIVEN NAMES | Peter D |
| FAMILY NAME | Hoff |
| SIGNATURE | HOFF P D |
| AFFILIATIONS | University of Washington |
| ORCID | 0000-0001-8041-0322 |
| VERIFIED | Yes |
| TOTAL WORKS | 13 |
| TOTAL CITATIONS | 153 |
| AUTHOR COUNT | 13 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 1998 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 5 |
Decomposing Network Influence: Social Influence Regression
Understanding network influence and its determinants are key challenges in political science and network analysis. Traditional latent variable models position actors within a social space based on network dependencies but often do not elucidate the underlying factors driving these interactions. To overcome this limitation, we propose the social influence regression (SIR) model, an extension of vector autoregression tailored for relational data th…
Multiplicative coevolution regression models for longitudinal networks and nodal attributes
Inferential Approaches for Network Analysis: Amen for Latent Factor Models
We introduce a Bayesian approach to conduct inferential analyses on dyadic data while accounting for interdependencies between observations through a set of additive and multiplicative effects (AME). The AME model is built on a generalized linear modeling framework and is thus flexible enough to be applied to a variety of contexts. We contrast the AME model to two prominent approaches in the literature: the latent space model (LSM) and the expone…
A new approach to analyzing coevolving longitudinal networks in international relations
Previous models of international conflict have suffered two shortfalls. They tend not to embody dynamic changes, focusing rather on static slices of behavior over time across a single relational dimension. These models have also been empirically evaluated in ways that assumed the independence of each country, when in reality they are searching for the interdependence among all countries. A number of approaches are available now for analyzing rela…
Likelihoods for fixed rank nomination networks
Many studies that gather social network data use survey methods that lead to censored, missing, or otherwise incomplete information. For example, the popular fixed rank nomination (FRN) scheme, often used in studies of schools and businesses, asks study participants to nominate and rank at most a small number of contacts or friends, leaving the existence of other relations uncertain. However, most statistical models are formulated in terms of com…
Representing degree distributions, clustering, and homophily in social networks with latent cluster random effects models
Modeling HIV transmission risk among Mozambicans prior to their initiating highly active antiretroviral therapy
Understanding sexual behavior and assessing transmission risk among people living with HIV-1 is crucial for effective HIV-1 prevention. We describe sexual behavior among HIV-positive persons initiating highly active antiretroviral therapy (HAART) in Beira, Mozambique. We present a Bernoulli process model (tool available online) to estimate the number of sexual partners who would acquire HIV-1 as a consequence of sexual contact with study particip…
Persistent Patterns of International Commerce
The authors examine a standard gravity model of international commerce augmented to include political as well as institutional influences on bilateral trade. Using annual data from 1980-2001, they estimate regression coefficients and residual dependencies using a hierarchy of models in each year. Rather than gauge the generalizability of these patterns via traditional measures of statistical significance such as p-values, this article develops an…
Assessing Antiretroviral Adherence via Electronic Drug Monitoring and Self-Report: An Examination of Key Methodological Issues
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 and inference. In an example analysis of international …
Modeling Dependencies in International Relations Networks
Despite the desire to focus on the interconnected nature of politics and economics at the global scale, most empirical studies in the field of international relations assume not only that the major actors are sovereign, but also that their relationships are portrayed in data that are modeled as independent phenomena. In contrast, this article illustrates the use of linear and bilinear random—effects models to represent statistical dependencies th…
Latent Space Approaches to Social Network Analysis
Network models are widely used to represent relational information among interacting units. In studies of social networks, recent emphasis has been placed on random graph models where the nodes usually represent individual social actors and the edges represent the presence of a specified relation between actors. We develop a class of models where the probability of a relation between actors depends on the positions of individuals in an unobserved…
Geschichte des deutschen Fernsehens
Modeling Dependencies in International Relations Networks
Despite the desire to focus on the interconnected nature of politics and economics at the global scale, most empirical studies in the field of international relations assume not only that the major actors are sovereign, but also that their relationships are portrayed in data that are modeled as independent phenomena. In contrast, this article illustrates the use of linear and bilinear random—effects models to represent statistical dependencies th…
Inferential Approaches for Network Analysis: Amen for Latent Factor Models
We introduce a Bayesian approach to conduct inferential analyses on dyadic data while accounting for interdependencies between observations through a set of additive and multiplicative effects (AME). The AME model is built on a generalized linear modeling framework and is thus flexible enough to be applied to a variety of contexts. We contrast the AME model to two prominent approaches in the literature: the latent space model (LSM) and the expone…
Persistent Patterns of International Commerce
The authors examine a standard gravity model of international commerce augmented to include political as well as institutional influences on bilateral trade. Using annual data from 1980-2001, they estimate regression coefficients and residual dependencies using a hierarchy of models in each year. Rather than gauge the generalizability of these patterns via traditional measures of statistical significance such as p-values, this article develops an…
A new approach to analyzing coevolving longitudinal networks in international relations
Previous models of international conflict have suffered two shortfalls. They tend not to embody dynamic changes, focusing rather on static slices of behavior over time across a single relational dimension. These models have also been empirically evaluated in ways that assumed the independence of each country, when in reality they are searching for the interdependence among all countries. A number of approaches are available now for analyzing rela…
Likelihoods for fixed rank nomination networks
Many studies that gather social network data use survey methods that lead to censored, missing, or otherwise incomplete information. For example, the popular fixed rank nomination (FRN) scheme, often used in studies of schools and businesses, asks study participants to nominate and rank at most a small number of contacts or friends, leaving the existence of other relations uncertain. However, most statistical models are formulated in terms of com…
Geschichte des deutschen Fernsehens
Latent Space Approaches to Social Network Analysis
Network models are widely used to represent relational information among interacting units. In studies of social networks, recent emphasis has been placed on random graph models where the nodes usually represent individual social actors and the edges represent the presence of a specified relation between actors. We develop a class of models where the probability of a relation between actors depends on the positions of individuals in an unobserved…
Modeling Dependencies in International Relations Networks
Despite the desire to focus on the interconnected nature of politics and economics at the global scale, most empirical studies in the field of international relations assume not only that the major actors are sovereign, but also that their relationships are portrayed in data that are modeled as independent phenomena. In contrast, this article illustrates the use of linear and bilinear random—effects models to represent statistical dependencies th…
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 and inference. In an example analysis of international …
Assessing Antiretroviral Adherence via Electronic Drug Monitoring and Self-Report: An Examination of Key Methodological Issues
Modeling HIV transmission risk among Mozambicans prior to their initiating highly active antiretroviral therapy
Understanding sexual behavior and assessing transmission risk among people living with HIV-1 is crucial for effective HIV-1 prevention. We describe sexual behavior among HIV-positive persons initiating highly active antiretroviral therapy (HAART) in Beira, Mozambique. We present a Bernoulli process model (tool available online) to estimate the number of sexual partners who would acquire HIV-1 as a consequence of sexual contact with study particip…
Persistent Patterns of International Commerce
The authors examine a standard gravity model of international commerce augmented to include political as well as institutional influences on bilateral trade. Using annual data from 1980-2001, they estimate regression coefficients and residual dependencies using a hierarchy of models in each year. Rather than gauge the generalizability of these patterns via traditional measures of statistical significance such as p-values, this article develops an…
Representing degree distributions, clustering, and homophily in social networks with latent cluster random effects models
Likelihoods for fixed rank nomination networks
Many studies that gather social network data use survey methods that lead to censored, missing, or otherwise incomplete information. For example, the popular fixed rank nomination (FRN) scheme, often used in studies of schools and businesses, asks study participants to nominate and rank at most a small number of contacts or friends, leaving the existence of other relations uncertain. However, most statistical models are formulated in terms of com…
A new approach to analyzing coevolving longitudinal networks in international relations
Previous models of international conflict have suffered two shortfalls. They tend not to embody dynamic changes, focusing rather on static slices of behavior over time across a single relational dimension. These models have also been empirically evaluated in ways that assumed the independence of each country, when in reality they are searching for the interdependence among all countries. A number of approaches are available now for analyzing rela…
Multiplicative coevolution regression models for longitudinal networks and nodal attributes
Inferential Approaches for Network Analysis: Amen for Latent Factor Models
We introduce a Bayesian approach to conduct inferential analyses on dyadic data while accounting for interdependencies between observations through a set of additive and multiplicative effects (AME). The AME model is built on a generalized linear modeling framework and is thus flexible enough to be applied to a variety of contexts. We contrast the AME model to two prominent approaches in the literature: the latent space model (LSM) and the expone…
Decomposing Network Influence: Social Influence Regression
Understanding network influence and its determinants are key challenges in political science and network analysis. Traditional latent variable models position actors within a social space based on network dependencies but often do not elucidate the underlying factors driving these interactions. To overcome this limitation, we propose the social influence regression (SIR) model, an extension of vector autoregression tailored for relational data th…
Computer Science (11 works) · Mathematics (10 works) · Opinion Dynamics and Social Influence (6 works) · Complex Network Analysis Techniques (5 works) · Statistics (5 works) · Artificial Intelligence (4 works) · Econometrics (4 works) · Political science (4 works) · Data mining (3 works) · Graph (3 works)