Provided by:
S.ANNA
From:
Altro PhD (Economics)
Lecturers:
Francesca CHIAROMONTE
Semester:
2
Hours:
10
Exam:
N
Educational Goals:
This course examines: (i) Computational assessment of statistical procedures, with resampling, cross-validation, permutations and perturbations. (ii) High dimensional supervised problems, with shrinkage, sparsification (e.g., Ridge, LASSO) and other feature selection techniques. (iii) Ultra-high dimensional supervised problems, with model-based and model free feature screening algorithms. (iv) Ultra-high sample sizes, with subsampling and partitioning strategies typically used for big data, and various considerations about significance and effect sizes. While not a pre-requisite, the course Topics in Statistical Learning provides important background for this course.
