This course aims to introduce students to the fundamental principles and methods underlying the statistical analysis of data. Another goal of study is to acquaint students with classical likelihood inference plus modern topics and efficient statistical inference procedures used in practice.
In this course students learn how to draw the right conclusions and interpretations about a model that has generated your data set.
Contents
Roles of Modeling in Statistical Inference, Principles of Data Reduction,
Estimation: Risk, Loss of estimators, Cramer-Rao inequality, M-estimation/Generalized estimating equations
Large sample properties: asymptotic distributions, relative efficiency, consistency
Likelihood-Based Methods: likelihood construction and estimation, Fisher Information, likelihood-based tests and confidence regions
Modes of study
Option
1
Available for:
Degree Programme Students
Other Students
Open University Students
Doctoral Students
Exchange Students
Participation in course work
In
English
Evaluation
Numeric 1-5.
Study materials
1. Essential Statistical Inference: Theory and Methods, Springer by Dennis D. Boos and L.A Stefanski 2. Statistical Inference 2nd Ed., Duxbury by G. Casella and R. Berger 3. Mathematical Statistics, Basic Ideas and Selected Topics by Peter J. Bickel, Kjell A. Doksum