| Academic Year |
2025Year |
School/Graduate School |
School of Informatics and Data Science |
| Lecture Code |
KA217001 |
Subject Classification |
Specialized Education |
| Subject Name |
ノンパラメトリック解析 |
Subject Name
（Katakana） |
ノンパラメトリックカイセキ |
Subject Name in
English |
Nonparametric analysis |
| Instructor |
YANAGIHARA HIROKAZU |
Instructor
(Katakana) |
ヤナギハラ ヒロカズ |
| Campus |
Higashi-Hiroshima |
Semester/Term |
3rd-Year, Second Semester, Intensive |
| Days, Periods, and Classrooms |
(Int) Inte |
| Lesson Style |
Lecture |
Lesson Style
(More Details) |
Face-to-face |
Lecture and exercise.
|
| Credits |
2.0 |
Class Hours/Week |
|
Language of Instruction |
J
:
Japanese |
| Course Level |
3
:
Undergraduate High-Intermediate
|
| Course Area（Area） |
25
:
Science and Technology |
| Course Area（Discipline） |
01
:
Mathematics/Statistics |
| Eligible Students |
|
| Keywords |
|
| Special Subject for Teacher Education |
|
Special Subject |
|
Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students) | |
|---|
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students) | Computer Science Program
（Abilities and Skills）
・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value.
Data Science Program
（Knowledge and Understanding）
・D1. Knowledge and ability to understand the theoretical framework of statistics and data analysis and to analyze qualitative/quantitative information of big data accurately and efficiently.
（Abilities and Skills）
・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value.
Intelligence Science Program
（Abilities and Skills）
・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value. |
|---|
Class Objectives
/Class Outline |
To understand and be able to do nonparametric analysis. |
| Class Schedule |
lesson1:Polynomial regression
lesson2:Optimization for the orders in polynomial regression
lesson3:R Introduction
lesson4:Exercises for polynomial regression
lesson5:Smoothing via local regression
lesson6:Local regression and hat matrices
lesson7:Spline functions
lesson8:Penalized spline regression
lesson9:Penalized spline regression and hat matrices
lesson10:Varying coefficient models and geographically weighted regression
lesson11:CV and GCV for selecting hyperparameter
lesson12:AIC and AICc for selecting hyperparameter
lesson13:Exercises for local regression
lesson14:Exercises for penalized spline regression
lesson15:Exercises for geographically weighted regression
reports |
Text/Reference
Books,etc. |
Handout |
PC or AV used in
Class,etc. |
Handouts, Microsoft Teams, moodle |
| (More Details) |
PC (Laptop as a Requisite Tool) |
| Learning techniques to be incorporated |
Quizzes/ Quiz format |
Suggestions on
Preparation and
Review |
Ask questions soon in the class or after the class if you cannot understand |
| Requirements |
|
| Grading Method |
reports |
| Practical Experience |
|
| Summary of Practical Experience and Class Contents based on it |
|
| Message |
|
| Other |
Since we will be using R in the exercises, please download it before the lecture (either R or R studio is fine).
However, you may use a different program as long as you can do the calculations.
Please note that we will not be able to answer any questions about programming languages if you use a different program. |
Please fill in the class improvement questionnaire which is carried out on all classes.
Instructors will reflect on your feedback and utilize the information for improving their teaching. |