Shahryar Minhas

Assistant Professor of Political Science Michigan State University

  • East Lansing MI

Shahryar Minhas is an expert in international political economy and conflict studies.

Contact

Michigan State University

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Biography

Shahryar Minhas is an Assistant Professor at Michigan State University in the Department of Political Science and the Social Science Data Analytics Program (SSDA).

Industry Expertise

International Affairs
Military
Education/Learning

Areas of Expertise

Network Analysis
Conflict Studies
International Relations
Forecasting & Modeling

Education

Duke University

Ph.D.

Politcal Science

Duke University

M.A.

Computational Economics

Journal Articles

Modeling Asymmetric Relationships from Symmetric Networks

Political Analysis

Many relationships requiring mutual agreement between pairs of actors produce observable networks that are symmetric and undirected. Nevertheless the unobserved, asymmetric network is often of primary scientific interest. We propose a method that probabilistically reconstructs the unobserved, asymmetric network from the observed, symmetric graph using a regression-based framework that allows for inference on predictors of actors' decisions. We apply this model to the bilateral investment treaty network. Our approach extracts politically relevant information about the network structure that is inaccessible to alternative approaches and has superior predictive performance.

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Splitting It Up: The Spduration Split-Population Duration RegressionPackage for Time-Varying Covariates

The R Journal

We present an implementation of split-population duration regression in the spduration (Beger et al., 2017) package for R that allows for time-varying covariates. The statistical model accounts for units that are immune to a certain outcome and are not part of the duration process the researcher is primarily interested in. We provide insights for when immune units exist, that can significantly increase the predictive performance compared to standard duration models. The package includes estimation and several post-estimation methods for split-population Weibull and log-logistic models. We provide an empirical application to data on military coups.

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Inferential Approaches for Network Analyses: AMEN for Latent Factor Models

Political Analysis

There is growing interest in the study of political networks. Network analysis allows scholars to move away from focusing on individual observations to the interrelationships among observations. Many network approaches have been developed in descriptive fashion, but attention to inferential approaches to network analysis has been growing. We introduce a new approach that models interdependencies among observations using additive and multiplicative effects (AME). This approach can be applied to binary, ordinal, and continuous network data, and provides a set of tools for inference from longitudinal networks as well. We review this approach and compare it to those examined in the recent survey by Cranmer et al. (2016). The AME approach is shown a) to be easy to implement; b) interpretable in a general linear model framework; c) computationally straightforward; d) not prone to degeneracy; e) captures 1st, 2nd, and 3rd order network dependencies; and f) notably outperforms multiple regression quadratic assignment procedures, exponential random graph models, and alternative latent space approaches on a variety of metrics and in an out-of-sample context. In summary, AME offers a straightforward way to undertake nuanced, principled inferential network analysis for a wide range of social science questions.

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