Semiparametric Models - 097470
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Lecture |
Exercise |
Laboratory |
Project or
Seminar |
House
Work |
Weekly
Hours |
2 |
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2 |
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Determination of the grade according to progress during the semester and a final examination.
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Prerequisites:
| | | | Statistical Theory for Data Analysis |
097414
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or
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(
| | Introduction to Statistics |
094423
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and
| Algebraic Methods for Data Science |
095295
| ) |
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or
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(
| | Introduction to Statistics |
094423
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and
| Numerical Algorithms |
234125
| ) |
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or
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(
| | Introduction to Statistics |
094423
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| | |
and
| Modern Algebra H |
104134
| ) |
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or
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(
| | Introduction to Statistics |
094423
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| | |
and
| Algebra B |
104168
| ) |
The Course Presents the Theory of Semiparametric Models and Show How to Apply This Theory to Solve Statistical Problems. the First Part of the Course Deals with the Definition of Semiparametric Models and the Theoretical Development for Estimators of the Parameters in These Models. the Second Part of the Course Will Focus on the Use of Semiparmatric Tools for Missing-Data Problems. at the End of the Course Students Will Know:
1. How to Define the Statistical Model in Semiparametric Problems,
2. How to Define Hilbert Spaces of Functions,
3. How to Develop Semiparametic Estimators, and to Analyze Their Theoretical Properties.
Textbooks| Published | Publisher | Authors | Book |
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| 2006 | springer | anastasios tsiatis | semiparametric theory and missing data |
Created in 06/03/2021 Time 01:43:12
