/tags/2021-winter/index.xml 2021 Winter - McGill Statistics Seminars

2021 Winter

  • Dependence Modeling of Mixed Insurance Claim Data

    Lu Yang · Apr 9, 2021

    Date: 2021-04-09

    Time: 15:30-16:30 (Montreal time)

    Zoom Link

    Meeting ID: 843 0865 5572

    Passcode: 690084

    Abstract:

    Multivariate claim data are common in insurance applications, e.g. claims of each policyholder for different types of insurance coverages. Understanding the dependencies among such multivariate risks is essential for the solvency and profitability of insurers. Effectively modeling insurance claim data is challenging due to their special complexities. At the policyholder level, claims data usually follow a two-part mixed distribution: a probability mass at zero corresponding to no claim and an otherwise positive claim from a skewed and long-tailed distribution. Copula models are often employed in order to simultaneously model the relationship between outcomes and covariates while flexibly quantifying the dependencies among the different outcomes. However, due to the mixed data feature, specification of copula models has been a problem. We fill this gap by developing a consistent nonparametric copula estimator for mixed data. Under our framework, both the models for the i) marginal relationship between covariates and claims and ii) dependence structure between claims can be chosen in a principled way. We show the uniform convergence of the proposed nonparametric copula estimator. Using the claim data from the Wisconsin Local Government Property Insurance Fund, we illustrate that our nonparametric copula estimator can assist analysts in identifying important features of the underlying dependence structure, revealing how different claims or risks are related to one another.

  • Learning Causal Structures via Continuous Optimization

    Simon Lacoste-Julien · Mar 26, 2021

    Date: 2021-03-26

    Time: 15:30-16:30 (Montreal time)

    Zoom Link

    Meeting ID: 843 0865 5572

    Passcode: 690084

    Abstract:

    There has been a recent surge of interest in the machine learning community in developing causal models that handle the effect of interventions in a system. In this talk, I will consider the problem of learning (estimating) a causal graphical model from data. The search over possible directed acyclic graphs modeling the causal structure is inherently combinatorial, but I’ll describe our recent work which use gradient-based continuous optimization for learning both the parameters of the distribution and the causal graph jointly, and can be combined naturally with flexible parametric families that use neural networks.

  • Measuring timeliness of annual reports filing by jump additive models

    Yicheng Kang · Mar 19, 2021

    Date: 2021-03-19

    Time: 15:30-16:30 (Montreal time)

    Zoom Link

    Meeting ID: 843 0865 5572

    Passcode: 690084

    Abstract:

    Foreign public issuers (FPIs) are required by the Securities and Exchanges Commission (SEC) to file Form 20-F as comprehensive annual reports. In an effort to increase the usefulness of 20-Fs, the SEC recently enacted a regulation to accelerate the deadline of 20-F filing from six months to four months after the fiscal year-end. The rationale is that the shortened reporting lag would improve the informational relevance of 20-Fs. In this work we propose a jump additive model to evaluate the SEC’s rationale by investigating the relationship between the timeliness of 20-F filing and its decision usefulness using the market data. The proposed model extends the conventional additive models to allow possible discontinuities in the regression functions. We suggest a two-step jump-preserving estimation procedure and show that it is statistically consistent. By applying the procedure to the 20-F study, we find a moderate positive association between the magnitude of the market reaction and the filing timeliness when the acceleration is less than 17 days. We also find that the market considers the filings significantly more informative when the acceleration is more than 18 days and such reaction tapers off when the acceleration exceeds 40 days.

  • Nonparametric Tests for Informative Selection in Complex Surveys

    Jay Breidt · Mar 12, 2021

    Date: 2021-03-12

    Time: 15:30-16:30 (Montreal time)

    Zoom Link

    Meeting ID: 939 8331 3215

    Passcode: 096952

    Abstract:

    Informative selection, in which the distribution of response variables given that they are sampled is different from their distribution in the population, is pervasive in complex surveys. Failing to take such informativeness into account can produce severe inferential errors, including biased and inconsistent estimation of population parameters. While several parametric procedures exist to test for informative selection, these methods are limited in scope and their parametric assumptions are difficult to assess. We consider two classes of nonparametric tests of informative selection. The first class is motivated by classic nonparametric two-sample tests. We compare weighted and unweighted empirical distribution functions and obtain tests for informative selection that are analogous to Kolmogorov-Smirnov and Cramer-von Mises. For the second class of tests, we adapt a kernel-based learning method that compares distributions based on their maximum mean discrepancy. The asymptotic distributions of the test statistics are established under the null hypothesis of noninformative selection. Simulation results show that our tests have power competitive with existing parametric tests in a correctly specified parametric setting, and better than those tests under model misspecification. A recreational angling application illustrates the methodology.

  • CoinPress: Practical Private Point Estimation and Confidence Intervals

    Gautam Kamath · Feb 26, 2021

    Date: 2021-02-26

    Time: 15:30-16:30 (Montreal time)

    Zoom Link

    Meeting ID: 843 0865 5572

    Passcode: 690084

    Abstract:

    We consider point estimation and generation of confidence intervals under the constraint of differential privacy. We provide a simple and practical framework for these tasks in relatively general settings. Our investigation addresses a novel challenge that arises in the differentially private setting, which involves the cost of weak a priori bounds on the parameters of interest. This framework is applied to the problems of Gaussian mean and covariance estimation. Despite the simplicity of our method, we are able to achieve minimax near-optimal rates for these problems. Empirical evaluations, on the problems of mean estimation, covariance estimation, and principal component analysis, demonstrate significant improvements in comparison to previous work.

  • Joint integrative analysis of multiple data sources with correlated vector outcomes

    Emily Hector · Feb 19, 2021

    Date: 2021-02-19

    Time: 15:30-16:30 (Montreal time)

    Zoom Link

    Meeting ID: 843 0865 5572

    Passcode: 690084

    Abstract:

    We consider the joint estimation of regression parameters from multiple potentially heterogeneous data sources with correlated vector outcomes. The primary goal of this joint integrative analysis is to estimate covariate effects on all vector outcomes through a marginal regression model in a statistically and computationally efficient way. We present a general class of distributed estimators that can be implemented in a parallelized computational scheme. Modelling, computational and theoretical challenges are overcome by first fitting a local model within each data source and then combining local results while accounting for correlation between data sources. This approach to distributed estimation and inference is formulated using Hansen’s generalized method of moments but implemented via an asymptotically equivalent and communication-efficient meta-estimator. We show both theoretically and numerically that the proposed method yields efficiency improvements and is computationally fast. We illustrate the proposed methodology with the joint integrative analysis of metabolic pathways in a large multi-cohort study.

  • Spatio-temporal methods for estimating subsurface ocean thermal response to tropical cyclones

    Mikael Kuusela · Feb 12, 2021

    Date: 2021-02-12

    Time: 15:30-16:30 (Montreal time)

    Zoom Link

    Meeting ID: 939 8331 3215

    Passcode: 096952

    Abstract:

    Tropical cyclones (TCs), driven by heat exchange between the air and sea, pose a substantial risk to many communities around the world. Accurate characterization of the subsurface ocean thermal response to TC passage is crucial for accurate TC intensity forecasts and for understanding the role TCs play in the global climate system, yet that characterization is complicated by the high-noise ocean environment, correlations inherent in spatio-temporal data, relative scarcity of in situ observations and the entanglement of the TC-induced signal with seasonal signals. We present a general methodological framework that addresses these difficulties, integrating existing techniques in seasonal mean field estimation, Gaussian process modeling, and nonparametric regression into a functional ANOVA model. Importantly, we improve upon past work by properly handling seasonality, providing rigorous uncertainty quantification, and treating time as a continuous variable, rather than producing estimates that are binned in time. This functional ANOVA model is estimated using in situ subsurface temperature profiles from the Argo fleet of autonomous floats through a multi-step procedure, which (1) characterizes the upper ocean seasonal shift during the TC season; (2) models the variability in the temperature observations; (3) fits a thin plate spline using the variability estimates to account for heteroskedasticity and correlation between the observations. This spline fit reveals the ocean thermal response to TC passage. Through this framework, we obtain new scientific insights into the interaction between TCs and the ocean on a global scale, including a three-dimensional characterization of the near-surface and subsurface cooling along the TC storm track and the mixing-induced subsurface warming on the track’s right side. Joint work with Addison Hu, Ann Lee, Donata Giglio and Kimberly Wood.

  • An Adaptive Algorithm to Multi-armed Bandit Problem with High-dimensional Covariates

    Wei Qian · Feb 5, 2021

    Date: 2021-02-05

    Time: 15:30-16:30 (Montreal time)

    Zoom Link

    Meeting ID: 843 0865 5572

    Passcode: 690084

    Abstract:

    This work studies an important sequential decision making problem known as the multi-armed bandit problem with covariates. Under a linear bandit framework with high-dimensional covariates, we propose a general arm allocation algorithm that integrates both arm elimination and randomized assignment strategies. By employing a class of high-dimensional regression methods for coefficient estimation, the proposed algorithm is shown to have near optimal finite-time regret performance under a new study scope that requires neither a margin condition nor a reward gap condition for competitive arms. Based on synergistically verified benefit of the margin, our algorithm exhibits an adaptive performance that automatically adapts to the margin and gap conditions, and attains the optimal regret rates under both study scopes, without or with the margin, up to a logarithmic factor. The proposed algorithm also simultaneously generates useful coefficient estimation output for competitive arms and is shown to achieve both estimation consistency and variable selection consistency. Promising empirical performance is demonstrated through two real data evaluation examples in drug dose assignment and news article recommendation.

  • Small Area Estimation in Low- and Middle-Income Countries

    Jon Wakefield · Jan 29, 2021

    Date: 2021-01-29

    Time: 15:30-16:30 (Montreal time)

    Zoom Link

    Meeting ID: 939 8331 3215

    Passcode: 096952

    Abstract:

    The under-five mortality rate (U5MR) is a key barometer of the health of a nation. Unfortunately, many people living in low- and middle-income countries are not covered by civil registration systems. This makes estimation of the U5MR, particularly at the subnational level, difficult. In this talk, I will describe models that have been developed to produce the official United Nations (UN) subnational U5MR estimates in 22 countries. Estimation is based on household surveys, which use stratified, two-stage cluster sampling. I will describe a range of area- and unit-level models and describe the rationale for the modeling we carry out. Data sparsity in time and space is a key challenge, and smoothing models are vital. I will discuss the advantages and disadvantages of discrete and continuous spatial models, in the context of estimation at the scale at which health interventions are made. Other issues that will be touched upon include: design-based versus model-based inference; adjustments for HIV epidemics; the inclusion of so-called indirect (summary birth history) data; reproducibility through software availability; benchmarking; how to deal with incomplete geographical data; and working with the UN to produce estimates.

  • Large-scale Machine Learning Algorithms for Biomedical Data Science

    Wei Zhang · Jan 15, 2021

    Date: 2021-01-15

    Time: 15:30-16:30 (Montreal time)

    Zoom Link

    Meeting ID: 843 0865 5572

    Passcode: 690084

    Abstract:

    During the last decade, hundreds of machine learning methods have been developed for disease outcome prediction based on high-throughput genomics data. However, the quality of the input genomics features and the output clinical variables has been ignored in these algorithms. In this talk, I will introduce two studies that develop methods to learn more accurate molecular signatures and drug response values for cancer research. These studies are supported by NSF, NIH, and Moffitt Cancer Center.