Statistics in Action: A Canadian Outlook

Commissioned by the Statistical Society of Canada (SSC), Statistics in Action: A Canadian Outlook helps both general readers and users of statistics better appreciate the scope and importance of statistics. It presents the ways in which statistics is used while highlighting key contributions that Canadian statisticians are making to science, technology, business, government, and other areas. The book emphasizes the role and impact of computing in statistical modeling and analysis, including the issues involved with the huge amounts of data being generated by automated processes.

The first two chapters review the development of statistics as a discipline in Canada and describe some major contributions to survey methodology made by Statistics Canada, one of the world’s premier official statistics agencies. The book next discusses how statistical methodologies, such as functional data analysis and the Metropolis algorithm, are applied in a wide variety of fields, including risk management and genetics. It then focuses on the application of statistical methods in medicine and public health as well as finance and e-commerce. The remainder of the book addresses how statistics is used to study critical scientific areas, including difficult-to-access populations, endangered species, climate change, and agricultural forecasts.

About the SSC

Founded in Montréal in 1972, the SSC is the main professional organization for statisticians and related professionals in Canada. Its mission is to promote the use and development of statistics and probability. The SSC publishes the bilingual quarterly newsletter SSC Liaison and the peer-reviewed scientific journal The Canadian Journal of Statistics. More information can be found at www.ssc.ca.

Table of Contents

Chapter 1: Canadians Studying Abroad and the Development of Statistics in Canada
Chapter 2: Some of Statistics Canada’s Contributions to Survey Methodology
Chapter 3: Watching Children Grow Taught Us All We Know
Chapter 4: Modeling Dependence beyond Correlation
Chapter 5: Lasso and Sparsity in Statistics
Chapter 6: Optimizing and Adapting the Metropolis Algorithm
Chapter 7: Design of Computer Experiments for Optimization, Estimation of Function Contours, and Related Objectives
Chapter 8: Statistical Genetic Modeling and Analysis of Complex Traits
Chapter 9: Bayesian Methods in Fisher’s Statistical Genetics World
Chapter 10: Bayesian Statistical Methodology for Observational Health Sciences Data
Chapter 11: Statistical Models for Disease Processes:Markers and Skeletal Complications in Cancer Metastatic to Bone
Chapter 12: Analysis of Biased Survival Data: The Canadian Study of Health and Aging andbeyond
Chapter 13: Assessing the Effect on Survival of Kidney Transplantation with Higher-Risk Donor Kidneys
Chapter 14: Risk-Adjusted Monitoring of Outcomes in Health Care
Chapter 15: Statistics in Financial Engineering
Chapter 16: Making Personalized Recommendations in E-Commerce
Chapter 17: What Do Salmon and Injection Drug Users Have in Common?
Chapter 18: Capture–Recapture Methods for Estimating the Size of a Population: Dealing with Variable Capture Probabilities
Chapter 19: Challenges in Statistical Marine Ecology
Chapter 20: Quantifying the Human and Natural Contributions to Observed Climate Change
Chapter 21: Data Hungry Models in a Food Hungry World: An Interdisciplinary Challenge Bridged by Statistics