Inhalt

[ 951MATSASIU14 ] UE Advanced Statistical Inference

Versionsauswahl
Workload Education level Study areas Responsible person Hours per week Coordinating university
6 ECTS M1 - Master's programme 1. year Statistics Helga Wagner 2 hpw Johannes Kepler University Linz
Detailed information
Pre-requisites keine
Original study plan Master's programme Statistics 2017W
Objectives Practicing the theoretical concepts and methods acquired in the lecture "Advanced Statistical Inference"
Subject asymptotic evaluations: convergence concepts, central limit theorem, consistency, the delta method, asymptotic efficiency

generating a random sample: direct and indirect methods, accept-reject, MCMC, bootstrapping

robustness: break point, M-estimator, influence function

asymptotic tests: Wald, Lagrange multiplier, Chi-square

confidence intervals: pivots, pivoting the cdf, asymptotic intervals

decision theory: loss function, risk function, Bayes risk

copulas

Criteria for evaluation Presentation of solved homeworks.
Methods Discussion of homework
Language English
Study material Casella G. and Berger R.L. (2002). Statistical Inference.
Changing subject? No
Corresponding lecture 5MSMS2UE: UE Mathematische Statistik II (4 ECTS)
On-site course
Maximum number of participants 40
Assignment procedure Assignment according to priority