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Information
| Code | ISB552 |
| Name | Generalized Linear Models |
| Term | 2023-2024 Academic Year |
| Term | Spring |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | Türkçe |
| Level | Doktora Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Prof. Dr. MAHMUDE REVAN ÖZKALE |
| Course Instructor |
1 |
Course Goal / Objective
To gain knowledge of measurement and theory in the context of modern probability theory.
Course Content
Distributions from exponential family, maximum likelihood method, logistic and poisson regression models, linear mixed models
Course Precondition
none
Resources
Agresti, A. 2016. Foundations of Linear and Generalized Linear Models. (Wiley Series in Probability and Statistics) 1st Edition
Notes
lecture notes
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Develop and deepen the current and advanced knowledge in the field of statistics based on master qualifications by original thought and / or research and at the level of expertise and reach original definitions that will bring innovation to the field |
| LO02 | Understands the interdisciplinary interaction related to the science of statistics; it achieves original results using knowledge requiring expertise in analysis, synthesis and evaluation of new and complex ideas |
| LO03 | Evaluates, uses and transfers new information in the field of statistics with a systematic approach |
| LO04 | Gains high level skills to use research methods in studies related to statistics. |
| LO05 | Develops a new idea, method, design and / or application that brings innovation to the field of statistics by using acquired skills, or applies a known idea, method, design and / or application to a different field, researches, comprehends, designs and applies an original topic |
| LO06 | Contributes to progress in the field by conducting an original study that brings innovation to the field of statistics, develops a new idea, method, design and / or application, or applies a known idea, method, design and / or application |
| LO07 | Extends the boundaries of knowledge in its field by publishing at least one scientific article in the field of statistics in national and / or international refereed journals and / or producing or interpreting an original work. Makes leadership in environments that require the analysis of original and interdisciplinary problems |
| LO08 | Develops new ideas and methods related to the field by using high level mental processes such as creative and critical thinking, problem solving and decision making |
| LO09 | Defends original opinions in discussing the subjects in the field of statistics and uses the necessary languages and technologies to communicate effectively in the field |
| LO10 | Uses the knowledge, problem solving and / or application skills that they assimilate in the field of statistics in interdisciplinary studies |
| LO11 | Develops strategy, policy and implementation plans and evaluates the results. Functional interaction by using strategic decision-making processes in solving problems encountered in relation to statistics |
| LO12 | Introduces scientific, technological, social or cultural advancements in the field of statistics and contributes to the process of being a society of knowledge and sustaining it. In addition, it contributes to the solution of social, scientific, cultural and ethical problems encountered in the field of statistics and supports the development of these values |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Develops new methods and strategies in modeling statistical problems and generating problem-specific solutions. | 5 |
| PLO02 | Bilgi - Kuramsal, Olgusal | Can do detailed research on a specific subject in the field of statistics. | 4 |
| PLO03 | Bilgi - Kuramsal, Olgusal | Have a good command of statistical theory to contribute to the statistical literature. | 4 |
| PLO04 | Bilgi - Kuramsal, Olgusal | Can use the knowledge gained in the field of statistics in interdisciplinary studies. | 5 |
| PLO05 | Yetkinlikler - Öğrenme Yetkinliği | Can organize projects and events in the field of statistics. | |
| PLO06 | Yetkinlikler - Öğrenme Yetkinliği | Can perform the stages of creating a project, executing it and reporting the results. | 5 |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Have the ability of scientific analysis. | 4 |
| PLO08 | Bilgi - Kuramsal, Olgusal | Can produce scientific publications in the field of statistics. | 4 |
| PLO09 | Bilgi - Kuramsal, Olgusal | Have analytical thinking skills. | 5 |
| PLO10 | Yetkinlikler - Öğrenme Yetkinliği | Can follow professional innovations and developments both at national and international level. | 4 |
| PLO11 | Yetkinlikler - Öğrenme Yetkinliği | Can follow statistical literature. | |
| PLO12 | Beceriler - Bilişsel, Uygulamalı | Can improve his/her foreign language knowledge at the level of making publications and presentations in a foreign language. | 5 |
| PLO13 | Bilgi - Kuramsal, Olgusal | Can use information technologies at an advanced level. | 3 |
| PLO14 | Bilgi - Kuramsal, Olgusal | Have the ability to work individually and make independent decisions. | 5 |
| PLO15 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Have the qualities necessary for teamwork. | 3 |
| PLO16 | Bilgi - Kuramsal, Olgusal | Have a sense of professional and ethical responsibility. | 2 |
| PLO17 | Bilgi - Kuramsal, Olgusal | Acts in accordance with scientific ethical rules. | 2 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Distributions related to the normal distribution, quadratic forms, model fitting and fundamentals of model fitting | Reading the related references | Öğretim Yöntemleri: Anlatım |
| 2 | Notations for explanatory variables, exponential family and the properties of distributions in exponential family | Reading the related references | Öğretim Yöntemleri: Anlatım |
| 3 | Introduction to generalized linear models, important distributions in generalized linear models | Reading the related references | Öğretim Yöntemleri: Anlatım |
| 4 | Maximum likelihood estimation and examples, Quasi-likelihood | Reading the related references | Öğretim Yöntemleri: Anlatım |
| 5 | Sampling distribution for score statistics, Taylor series approximations, sampling distibution for maximum likelihood distributions | Reading the related references | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 6 | Log-likelihood ratio statistic, sampling distibution for deviance, hypothesis testing | Reading the related references | Öğretim Yöntemleri: Anlatım |
| 7 | Residual analysis in generalized linear models | Reading the related references | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 8 | Mid-Term Exam | Review the topics discussed in the lecture notes and sources | Ölçme Yöntemleri: Ödev |
| 9 | Binary variables and logistic regression | Reading the related references | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 10 | Nominal and ordinal logistic regression | Reading the related references | Öğretim Yöntemleri: Anlatım |
| 11 | Poisson regression | Reading the related references | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 12 | Linear mixed models, estimation in linear mixed models | Reading the related references | Öğretim Yöntemleri: Anlatım |
| 13 | Inference for regression coefficients and variance components in linear mixed models | Reading the related references | Öğretim Yöntemleri: Anlatım |
| 14 | Conditional and marginal expectations in linear mixed models, diagnostic measures | Reading the related references | Öğretim Yöntemleri: Anlatım |
| 15 | Generalized linear mixed models | Reading the related references | Öğretim Yöntemleri: Anlatım |
| 16 | Application on generalized linear models | Review the topics discussed in the lecture notes and sources | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Term Exams | Review the topics discussed in the lecture notes and sources | Ölçme Yöntemleri: Yazılı Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 3 | 42 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 5 | 70 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 15 | 15 |
| Final Exam | 1 | 30 | 30 |
| Total Workload (Hour) | 157 | ||
| Total Workload / 25 (h) | 6,28 | ||
| ECTS | 6 ECTS | ||