CRM-Colloquium

Date: 2022-02-18 Time: 15:30-16:30 (Montreal time) https://umontreal.zoom.us/j/85105423917?pwd=enM3MGpFNkZKU2daMjRITmo0N0JUUT09 Meeting ID: 851 0542 3917 Passcode: 403790 Abstract: Extremal graphical models are sparse statistical models for multivariate extreme events. The underlying graph encodes conditional independencies and enables a visual interpretation of the complex extremal dependence structure. For the important case of tree models, we provide a data-driven methodology for learning the graphical structure. We show that sample versions of the extremal correlation and a new summary statistic, which we call the extremal variogram, can be used as weights for a minimum spanning tree to consistently recover the true underlying tree.

Date: 2022-01-28 Time: 15:30-16:30 (Montreal time) https://umontreal.zoom.us/j/93983313215?pwd=clB6cUNsSjAvRmFMME1PblhkTUtsQT09 Meeting ID: 939 8331 3215 Passcode: 096952 Abstract: Statistical risk assessment, in particular in finance and insurance, requires estimating simple indicators to summarize the risk incurred in a given situation. Of most interest is to infer extreme levels of risk so as to be able to manage high-impact rare events such as extreme climate episodes or stock market crashes. A standard procedure in this context, whether in the academic, industrial or regulatory circles, is to estimate a well-chosen single quantile (or Value-at-Risk).

Date: 2021-11-26 Time: 15:30-16:30 (Montreal time) Zoom Link Meeting ID: 939 8331 3215 Passcode: 096952 Abstract: Monitoring marked individuals is a common strategy in studies of wild animals (referred to as mark-recapture or capture-recapture experiments) and hard to track human populations (referred to as multi-list methods or multiple-systems estimation). A standard assumption of these techniques is that individuals can be identified uniquely and without error, but this can be violated in many ways.

Date: 2021-10-29 Time: 15:30-16:30 (Montreal time) Zoom Link Meeting ID: 939 8331 3215 Passcode: 096952 Abstract: In the data science courses at the University of British Columbia, we define data science as the study, development and practice of reproducible and auditable processes to obtain insight from data. While reproducibility is core to our definition, most data science learners enter the field with other aspects of data science in mind, for example predictive modelling, which is often one of the most interesting topic to novices.

Date: 2021-09-24 Time: 15:00-16:00 (Montreal time) https://mcgill.zoom.us/j/9791073141 Meeting ID: 979 107 3141 Abstract: In the data science courses at the University of British Columbia, we define data science as the study, development and practice of reproducible and auditable processes to obtain insight from data. While reproducibility is core to our definition, most data science learners enter the field with other aspects of data science in mind, for example predictive modelling, which is often one of the most interesting topic to novices.

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.

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.

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.

Date: 2020-11-13 Time: 15:30-16:30 Zoom Link Abstract: The error or variability of statistical and machine learning algorithms is often assessed by repeatedly re-fitting a model with different weighted versions of the observed data. The ubiquitous tools of cross-validation (CV) and the bootstrap are examples of this technique. These methods are powerful in large part due to their model agnosticism but can be slow to run on modern, large data sets due to the need to repeatedly re-fit the model.

Date: 2020-10-02 Time: 15:30-16:30 Zoom Link Meeting ID: 939 8331 3215 Passcode: 096952 Abstract: Data science is discussed along with some historical perspective. Selected problems in classification are considered, either via specific datasets or general problem types. In each case, the problem is introduced before one or more potential solutions are discussed and applied. The problems discussed include data with outliers, longitudinal data, and three-way data. The proposed approaches are generally mixture model-based.