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| TBB6008 | Statistical Analysis and Norm. with Missing Data | 3+0+0 | ECTS:7.5 | | Year / Semester | Fall Semester | | Level of Course | Third Cycle | | Status | Elective | | Department | DEPARTMENT of BIOSTATISTICS and MEDICAL INFORMATICS | | Prerequisites and co-requisites | None | | Mode of Delivery | Face to face | | Contact Hours | 14 weeks - 3 hours of lectures per week | | Lecturer | -- | | Co-Lecturer | | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | The first aim of this course is inspection of parametric and semi parametric methods which are used in missing data exploration and analysis. Second aim of the course is inspection of data normalization methods which preserve variability of data that came from different sources |
| Programme Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | PO - 1 : | Describe basic concepts of probability, random variation and commonly used statistical probability distributions | | | | PO - 2 : | Describe preferred methodological alternatives to commonly used statistical methods when assumptions are not met. | | | | PO - 3 : | Apply common statistical methods for inference. | | | | PO - 4 : | Apply parametric and semi parametric imputation techniques | | | | PO - 5 : | Describe and apply normalization methods on genetic data | | | | CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), PO : Learning Outcome | | |
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Introduction, and Naive Methods, Imputation and Multiple Imputation, Likelihood-based Method for Missing Data, EM algorithm and extensions, Robustness of Estimation with Missing Data, Introduction of Semiparametric Models with Missing Data, Semiparametric Efficiency Theory, Semiparametric Model with Missing data, Introduction to Data Normalization, Data Normalization on Gene Expression Data, Data Normalization for DNA Methylation Analysis
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Introduction and Naive Methods | | | Week 2 | Imputation and Multiple Imputation | | | Week 3 | Likelihood-based Method for Missing Data | | | Week 4 | EM algorithm and extensions | | | Week 5 | Robustness of Estimation with Missing Data | | | Week 6 | Introduction of Semiparametric Models with Missing Data | | | Week 7 | Midterm Exam | | | Week 8 | Semiparametric Efficiency Theory | | | Week 9 | Semiparametric Model with Missing data | | | Week 10 | Introduction to Data Normalization | | | Week 11 | Midterm Exam | | | Week 12 | Data Normalization on Gene Expression Data | | | Week 13 | Data Normalization for DNA Methylation Analysis | | | Week 14 | Presentations | | | Week 15 | Presentations | | | Week 16 | Final Exam | | | |
| 1 | Little, R.J.A. and Rubin, D.B.,2002;Statistical Analysis with Missing Data;John Wiley) | | | 2 | Tsiatis, A.A.,2006;Semiparametric Theory and Missing Data;Springer) | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 7 | | | 30 | | Quiz | 11 | | | 20 | | Presentation | 16 | | | 50 | | |
| Student Work Load and its Distribution | | Type of work | Duration (hours pw) | No of weeks / Number of activity | Hours in total per term | | Yüz yüze eğitim | 3 | 14 | 42 | | Sınıf dışı çalışma | 5 | 14 | 70 | | Ödev | 3 | 14 | 42 | | Dönem sonu sınavı için hazırlık | 2 | 16 | 32 | | Dönem sonu sınavı | 2 | 1 | 2 | | Total work load | | | 188 |
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