Explores the COVID-19 outbreak, its terminology, transmission, severity, and global impact, emphasizing the importance of mitigation strategies and digital epidemiology.
Explores statistical inference, sufficiency, and completeness, emphasizing the importance of sufficient statistics and the role of complete statistics in data reduction.
Focuses on large-scale inference for detecting QTL hotspots in sparse regression models, emphasizing the need to use genomics to understand variation in phenotypes and disease susceptibility.
Explores the challenges of inferring epidemiological parameters from clinical data, focusing on COVID-19 and the complexities of estimating infection fatality ratios.
Explores constructing confidence regions, inverting hypothesis tests, and the pivotal method, emphasizing the importance of likelihood methods in statistical inference.
Introduces statistical inference concepts, focusing on parameter estimation, unbiased estimators, and mean estimation using independent random variables.
Delves into the challenges and opportunities of machine learning in credit risk modeling, comparing traditional statistical models with machine learning methods.
