Contagion effects, also known as peer effects or social influence process, have become more and more central to social science, especially with the availability of longitudinal social network data. However, contagion effects are usually difficult to identify, as they are often entangled with other factors, such as homophily in the selection process, the individual’s preference for the same social settings, etc. Methods currently available either do not solve these problems or require strong assumptions. Following Shaliziand Thomas (2011), I frame this difficulty as an omitted variable bias problem, and I propose several alternative estimation methods that have potentials to correctly identify contagion effects when there is an unobserved trait that co-determines the influence and the selection. The Monte-Carlo simulation results suggest that a latent-space adjusted estimator is especially promising. It outperforms other estimators that are traditionally used to deal with the unobserved variables, including a structural equation based estimator and an instrumental variable estimator.

We conduct a quantitative risk analysis of non-core insurance business of selling protection to financial firms against investment losses due to a shock. A static structural model is constructed, composed of a network of firms who cross-hold each other, multiple primitive assets that are vulnerable to a shock, and an insurer who resides external to the network and speculates in selling protection to the financial firms. Assume that each firm in the network is rational and able to decide how much protection to purchase to optimize its portfolio according to the meanvariance principle. As a result, the shock may impact on the insurer but indirectly through the network. More precisely, the network integration, which refers to the level of exposures of the firms to each other, aspects the way that the shock impacts on this non-core insurance business. Our study finds that the network integration and the shock play an interactive role in the insurance risk: An increase in the network integration can either reduce or amplify the impact of the shock on the insurance risk.

Bio: Dr. Qihe Tang joined the UNSW Business School as a Full Professor under the Strategic Hires and Retention Pathways (SHARP) scheme in July 2017.

After earning his Ph.D. in Statistics from the University of Science and Technology of China in 2001, he has worked at different places in the world including the University of Hong Kong (2001), the University of Amsterdam (2002-2004), the Concordia University (2004-2005), and the University of Iowa (2006-2017). At the University of Iowa, he was promoted to Full Professor in July 2012, and he was conferred the F. Wendell Miller Endowed Professorship in July 2014 in honour of his scholarly work and professional contributions.

Qihe Tang’s expertise centers on extreme value theory for insurance, finance, and quantitative risk management. Recently, he has been working on various topics newly arising from the interdisciplinary area of insurance, finance, probability, and statistics. These topics include: (1) interplay of insurance and financial risks, (2) large credit portfolio losses, and (3) modeling, measuring, and managing catastrophe risks. His research on these topics has been constantly supported by external grants.

Qihe Tang has recently been elected as an editor for Insurance: Mathematics and Economics. Currently, he is also an associate editor for the journals TEST, Applied Stochastic Models in Business and Industry, and Statistics & Probability Letters, and serves on the editorial boards of the journals Risks and Dependence Modeling. He has graduated a number of doctoral students who are now university professors all over the world.

In the weighted logrank tests such as Fleming-Harrington test and the Tarone-Ware test, certain weights are used to put more weight on early, middle or late events. The purpose is to maximize the power of the test. The optimal weight under an alternative depends on the true hazard functions of the groups being compared, and thus cannot be applied directly. We propose replacing the true hazard functions with their estimates and then using the estimated weights in a weighted logrank test. However, the resulting test does not control type I error correctly because the weights converge to 0 under the null in large samples. We then adjust the estimated optimal weights for correct type I error control while the resulting test still achieves improved power compared to existing weighted logrank tests, and it is shown to be robust in various scenarios. Extensive simulation is carried out to assess the proposed method and it is applied in several clinical studies in lung cancer.

We compute doubly truncated moments for the selection elliptical (SE) class of distributions, which includes some multivariate asymmetric versions of well-known elliptical distributions, such as, the normal, Student’s t, among others. We address the moments for doubly truncated members of this family, establishing neat formulation for high order moments as well as for its first two moments. We establish sufficient and necessary conditions for their existence. Further, we propose computational efficient methods to deal with extreme settings of the parameters, partitions with almost zero volume or no truncation. Applications and simulation studies are presented in order to illustrate the usefulness of the proposed methods.


Bio: Dr. Christian Galarza is a professor in the faculty of Mathematics and Natural Science at Escuela Superior Politécnica del Litoral (ESPOL), in Guayaquil, Ecuador. He obtained his PhD degree in Statistics in 2020, from the State University of Campinas, UNICAMP, Brazil, where he obtained his Master degree in Statistics as well. He visited the University of Connecticut from 2018 to 2019 as a research scholar. His research interests include quantile regression, linear/nonlinear mixed-effects models, EM and SAEM algorithms, zero-quantile distributions, scale mixture of skew normal distributions and censored and zero-inflated models.

Rapid developments in streaming data technologies have enabled real-time monitoring of human activity. Wearable devices, such as wrist-worn sensors that monitor gross motor activity (actigraphy), have become prevalent. An actigraph unit continually records the activity level of an individual, producing large amounts of high-resolution measurements that can be immediately downloaded and analyzed. While this type of BIG DATA includes both spatial and temporal information, we argue that the underlying process is more appropriately modeled as a stochastic evolution through time, while accounting for spatial information separately. A key challenge is the construction of valid stochastic processes over paths. We devise a spatial-temporal modeling framework for massive amounts of actigraphy data, while delivering fully model-based inference and uncertainty quantification. Building upon recent developments in scalable inference, we construct temporal processes using directed acyclic graphs (DAG) and develop optimized implementations of collapsed Markov chain Monte Carlo (MCMC) algorithms for Bayesian inference. We test and validate our methods on simulated data and subsequently apply and verify their predictive ability on an original dataset from the Physical Activity through Sustainable Transport Approaches (PASTA-LA) study conducted by UCLA’s Fielding School of Public Health.

In this article, we apply non-convex regularization methods in order to obtain stable estimation of loss development factors in insurance claims reserving. Among the non-convex regularization methods, we focus on the use of the log-adjusted absolute deviation (LAAD) penalty and provide discussion on optimization of LAAD penalized regression model, which we prove to converge with a coordinate descent algorithm under mild conditions. This has the advantage of obtaining a consistent estimator for the regression coefficients while allowing for the variable selection, which is linked to the stable estimation of loss development factors. We calibrate our proposed model using a multi-line insurance dataset from a property and casualty insurer where we observed reported aggregate loss along accident years and development periods. When compared to other regression models, our LAAD penalized regression model provides very promising results.

Bio: Dr. Himchan Jeong is an Assistant Professor in the Department of Statistics and Actuarial Science at Simon Fraser University, Canada. He is a Fellow of the Society of Actuaries (SOA) and holds a Ph.D. from the University of Connecticut. He has been actively involved in teaching and conducting research in actuarial science for several years. In recognition for his academic achievements and excellence, he has been awarded the James C. Hickman Scholarship from SOA recently in 2018-2020.
His current research interest is predictive modeling for ratemaking and reserving of property and casualty insurance.

Consider the subgraph sampling model, where we observe a random subgraph of a given (possibly non random) large graph $G_n$, by choosing vertices of $G_n$ independently at random with probability $p_n$. In this setting, we study the question of estimating the number of copies $N(H,G_n)$ of a fixed motif/small graph (think of $H$ as edges, two stars, triangles) in the big graph $G_n$. We derive necessary and sufficient conditions for the consistency and the asymptotic normality of a natural Horvitz-Thompson (HT) type estimator. 

As it turns out, the asymptotic normality of the HT estimator exhibits an interesting fourth-moment phenomenon, which asserts that the HT estimator (appropriately centered and rescaled) converges in distribution to the standard normal whenever its fourth-moment converges to 3. We apply our results to several natural graph ensembles, such as sparse graphs with bounded degree, Erdős-Renyi random graphs, random regular graphs, and dense graphons.