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HU Credits:
6
Degree/Cycle:
1st degree (Bachelor)
Responsible Department:
Statistics
Semester:
1st Semester
Teaching Languages:
Hebrew
Campus:
Mt. Scopus
Course/Module Coordinator:
Ariel Jaffe
Coordinator Office Hours:
Sundays, 18:30-19:30
Teaching Staff:
Dr. Ariel Jaffe,
Ms. Alon Shira
Course/Module description:
Introduction to Statistical Theory
Course/Module aims:
To explain theoretical foundations behind statistical methods
Learning outcomes - On successful completion of this module, students should be able to:
to understand theoretical foundations of statistical inference
Attendance requirements(%):
None
Teaching arrangement and method of instruction:
In-class lectures and TA sessions.
Course/Module Content:
1. Statistical Models
Inferential versus descriptive statistics,
parametric and nonparametric models,
likelihood function,
identifiability,
sufficient statistic,
exponential families of distributions.
2. Estimation in parametric models
Refresh on estimation methods,
elements of Decision Theory, Bayesian estimation,
Unbiased estimation (Rao-Blackwell improvement, complete statistic and Lehmann–Scheffé theorem, Fisher information and Cramer-Rao bound),
large sample asymptotic estimation and confidence sets.
3. Hypotheses testing in parametric models
Elements of Decision Theory,
Neyman-Pearson lemma and the likelihood ration test,
examples of UMP tests,
Generalized likelihood ration test
Required Reading:
None
Additional Reading Material:
Lecture notes will be distributed during the course. Additional recommended reading:
Felix Abramovich and Ya'acov Ritov: Statistical Theory: A Concise Introduction
P.Bickel, K.Doksum, Mathematical Statistics: basic ideas and selected topics, 1977
Casella, George; Berger, Roger L. Statistical inference, 1990.
Grading Scheme :
Written / Oral / Practical Exam 65 %
Mid-terms exams 35 %
Additional information:
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