Learning outcomes

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

Evaluation

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

Belongs to following study modules

2018–2019

Teaching

Archived Teaching Schedule. Please refer to current Teaching Shedule.

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