401-4637-67L  On Hypothesis Testing

SemesterSpring Semester 2021
LecturersF. Balabdaoui
Periodicitynon-recurring course
Language of instructionEnglish

AbstractThis course is a review of the main results in decision theory.
ObjectiveThe goal of this course is to present a review for the most fundamental results in statistical testing. This entails reviewing the Neyman-Pearson Lemma for simple hypotheses and the Karlin-Rubin Theorem for monotone likelihood ratio parametric families. The students will also encounter the important concept of p-values and their use in some multiple testing situations. Further methods for constructing tests will be also presented including likelihood ratio and chi-square tests. Some non-parametric tests will be reviewed such as the Kolmogorov goodness-of-fit test and the two sample Wilcoxon rank test. The most important theoretical results will reproved and also illustrated via different examples. Four sessions of exercises will be scheduled (the students will be handed in an exercise sheet a week before discussing solutions in class).
Literature- Statistical Inference (Casella & Berger)
- Testing Statistical Hypotheses (Lehmann and Romano)