Explores semiparametric inference for missing-not-at-random data, addressing challenges in statistical analysis and proposing a doubly-robust estimator.
Delves into the challenges and opportunities of machine learning in credit risk modeling, comparing traditional statistical models with machine learning methods.
Explores constructing confidence regions, inverting hypothesis tests, and the pivotal method, emphasizing the importance of likelihood methods in statistical inference.
Explores statistical inference, sufficiency, and completeness, emphasizing the importance of sufficient statistics and the role of complete statistics in data reduction.
