Web-Based Lectures




Title: Making Inferences from Non-probability Samples through Data Integration
Presenter: Jean-François Beaumont, Statistics Canada
Date and Time: Tuesday, September 28, 12:00 p.m. – 2:00 p.m. Eastern Time
Sponsor: Survey Research Methods Section

Registration Deadline: Monday, September 27, at 12:00 p.m. Eastern time

Description:
For several decades, national statistical agencies around the world have been using probability surveys as their preferred tool to meet information needs about a population of interest. In the last few years, there has been a wind of change and other data sources are being increasingly explored. Five key factors are behind this trend: the decline in response rates in probability surveys, the high cost of data collection, the increased burden on respondents, the desire for access to “real-time” statistics, and the proliferation of non-probability data sources. In this presentation, I review some data integration approaches that take advantage of both probability and non-probability data sources. Some of these approaches rely on the validity of model assumptions, which contrasts with approaches based solely on the probability sampling design. These design-based approaches are generally not as efficient; yet, they are not subject to the risk of bias due to model misspecification.

Registration Fees:
SRMS Members: $20
ASA Members: $30
Student ASA Member: $25
Nonmembers: $45

Each registration is allowed one web connection. Sound is received via audio streaming from your computer’s speakers.

Register

Access Information

Registered persons will be sent an email the afternoon of Monday, September 27, with the information to join the webinar and, if possible, a link to download and print a copy of the presentation slides.



Title: Climate Change, Extremes, and Risk
Presenter: Richard L. Smith, Department of Statistical and Operations Research, University of North Carolina at Chapel Hill
Dates and Times: Wednesday, September 29, 2:00 p.m. - 4:00 p.m.
Sponsor: Section on Risk Analysis

Registration Deadline: Tuesday, September 28, at 12:00 p.m. Eastern time

Description:
2021 has been the year that climate change finally became a subject everyone was talking about. A series of extreme climate events have covered the US and Canada, many parts of Europe, and other parts of the world. The Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) delivers dire warnings about what will happen if we fail to curb greenhouse gas emissions quickly. An international conference will take place in Britain in November where world leaders including President Biden will be expected to reveal their plans for action. So, where do statistics and data science fit into this picture? It has often been stated that we cannot attribute a single event, such as the recent extreme heat conditions in the Pacific Northwest or the wildfires affecting many parts of the world, to climate change. What we can calculate is how the probability of such an event, or the size of the event given its occurrence, may change as a result of greenhouse gas emissions compared with the atmospheric conditions that existed 200 years ago. First, we need to define the event itself, for example, that the average temperature over a specific region of space and time exceeded a certain threshold level. Second, we can estimate the probability of such an event by studying historical records and comparing them with climate model output, in effect, computer simulations of climate under both present-day and historical conditions. Extreme value theory is the branch of statistics concerned with estimating probabilities of extreme events, and is widely used to characterize probabilities of extreme weather events. However, that theory itself raises many questions about the appropriate choice of distribution, method of estimating parameters, and how to account for uncertainty. This talk will introduce these concepts to statisticians and data scientists not previously familiar with this field. No prior knowledge of climate science will be assumed, and only a basic graduate-level knowledge of statistics. The talk will introduce extreme value theory, show how these methods are applied in the climate context, discuss some of the pitfalls, and suggest directions for future research. Richard Smith has been performing research in extreme value theory for several decades, has authored many papers on climatological statistics with research groups including the Statistical and Applied Mathematical Sciences Institute (SAMSI) and the National Center for Atmospheric Research (NCAR), and has interacted with numerous climate scientists in North America and worldwide. He has just been reappointed to the EPA’s Science Advisory Board.

Registration Fees:
Members of the Section on Risk Analysis: $20
ASA Members: $30
Student ASA Member: $25
Nonmembers: $45

Each registration is allowed one web connection. Sound is received via audio streaming from your computer’s speakers.

Register

Access Information

Registered persons will be sent an email the afternoon of Tuesday, September 28, with the information to join the webinar and, if possible, a link to download and print a copy of the presentation slides.



Title: Machine Learning Foundations: A Hands-on Introduction
Presenter: Dr. William Franz Lamberti
Dates and Times: This will be a three-part webinar presentation from 11:00 a.m. – 1:00 p.m. Eastern time on October 5th, 6th, and 7th. Register just once to receive the access information for all three sessions.

Registration Deadline: Monday, October 4, at 12:00 p.m. Eastern time

Description:
In this course, participants will be introduced to the foundational approaches in machine learning (ML). The emphasis of the course is introducing various algorithms in ML for a variety of tasks such as classification and prediction of continuous values. This course requires the skills presented in An Introduction to R for Non-Programmers (sponsored by ASA September 7-9 or https://vimeo.com/ondemand/rintro). Learning through examples will be greatly emphasized. Data analyzed is related to, but not limited to, Geography, Biology, Meteorology, and Image Analysis. The data is available on base R or GitHub. This course will be conducted virtually, so participants are highly encouraged to attend with "R-ready" computers (Windows, Mac, or Linux are all acceptable) to work on examples during the course. This course is designed for individuals who have little to no experience with ML. Familiarity with R programming is required.

Outline:
Day 1: 10/5/21: 11 AM EST
Hour 1: The Philosophy of Machine and Statistical Learning- Topics include differences between Machine and Statistical Learning in goals, interpretation, and presentation.
Hour 2: Linear Regression - Topics include linear and multiple regression from the perspective of a Machine Learning Analyst.

Day 2: 10/6/21: 11 AM EST
Hour 1: Support Vector Machines - Topics include support vector machines and various kernels such as the polynomial kernel.
Hour 2: Resampling Methods - Topics include random splits and cross-validation.

Day 3: 10/7/21: 11 AM EST
Hour 1: Tree-Based Methods – Topics include regression and classification trees.
Hour 2: LASSO, Discussion, and Questions - Topic is the least absolute shrinkage and selection operator (LASSO). This time will also be allotted for specific questions from the participants. If there is extra time, additional topics such as neural networks may be introduced.

Learning Objectives:
By the end of the course, participants will have been introduced to popular ML techniques. The material presented will provide the foundation to understand more complicated ML algorithms. The material provides the basic knowledge to learn about advanced techniques.

Content and Instructional Methods:
The course will have slides for the content and are available for the participants. However, many hands on examples are done throughout the course. Generally speaking, each hour will end with an example which summarizes the content for that hour. Participants will have an opportunity to work together to solve the example. The hour ends with presenting a solution to the example provided.

Presenter Background:
Dr. Lamberti (Ph.D., Computational Sciences and Informatics with a concentration in Data Science, M.S., Statistical Science) has given talks on R at NASA Langley Research Center, George Mason University’s Aspiring Scientists Summer Internship Program, the American Statistical Association’s (ASA’s) Joint Statistical Meetings, and ASA’s Professional Development Webinar Series. He has also received teaching honors as the 2016-2017 Outstanding Graduate Teaching Assistant at George Mason University Statistics Department. He is currently a Postdoctoral Associate at the University of Virginia. Examples of his talks at George Mason University are found at the following link: http://www.rgalleon.com/talks/

Registration Fees:
ASA Members: $85
Student ASA Member: $60
Nonmembers: $135

This presentation will use the Zoom webinar platform. Each registration is allowed one web connection. Sound is received via audio streaming from your computer’s speakers.

Register

Access Information

Registered persons will be sent an email the afternoon of Monday, October 4, with the information to join the webinars and, if possible, a link to download and print a copy of the presentation slides.