KA217001 Nonparametric analysis

Hiroshima University Syllabus

Japanese

Academic Year 2026Year 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 LOU YANG

Instructor
(Katakana) ロウ　ヤン

Campus Higashi-Hiroshima Semester/Term 3rd-Year, First Semester, 2Term

Days, Periods, and Classrooms (2T) Weds5-8：IAS K211

Lesson Style Lecture Lesson Style
(More Details) Face-to-face, Online (on-demand)

This is a lecture-based class, primarily held in person, with online (on demand) sessions available only in special situations, such as severe weather, the instructor’s business trip, or other exceptional circumstances.
The first lesson will be conducted in person.

Credits 2.0 Class Hours/Week 4 Language of Instruction E : English

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 The course introduces core concepts and methods of nonparametric analysis, focusing on statistical inference without parametric assumptions and practical applications to real data.

Class Schedule lesson1 Foundations of Statistical Inference
lesson2 Parametric vs Nonparametric Inference
lesson3 Permutation Logic and Binomial Tests
lesson4 Order Statistics, Ranks, and Data Exploration
lesson5 Single Sample Inference: The Sign Test
lesson6 Rank Based Inference for Medians
lesson7 Scores, Robustness, and Test Comparisons
lesson8 Empirical Distribution Functions and Distributional Tests
lesson9 Dichotomous Data, Trend, and Randomness
lesson10 Paired Samples and Matched Designs
lesson11 Two Independent Samples: Location Inference
lesson12 Two Independent Samples: Beyond Location
lesson13 Three or More Samples
lesson14 Structured Data and Survival Analysis
lesson15 Correlation, Concordance, and Synthesis

Text/Reference
Books,etc. N. C. Smeeton, N. H. Spencer, and P. Sprent. Applied Nonparametric Statistical Methods, (Fifth Edition). New York, NY, USA: Chapman and Hall/CRC, 2025.

PC or AV used in
Class,etc. Text, Handouts, moodle

(More Details)

Learning techniques to be incorporated Quizzes/ Quiz format, Post-class Report

Suggestions on
Preparation and
Review 1. Each lesson's materials (e.g., slides provided by the instructor) should be reviewed both before and after class.
2. The meaning of all English technical terms should be understood before class.

Requirements Prior coursework in inferential statistics is desirable, along with a working background in either R or Python programming.

Grading Method The grade will be based on the following two components:
1. in‑class tests, and
2. a final report.

Practical Experience

Summary of Practical Experience and Class Contents based on it

Message

Other Please bring your own PC, as we will be doing exercises.

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.

Academic Year	2026Year	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	LOU YANG
Instructor (Katakana)	ロウ　ヤン
Campus	Higashi-Hiroshima	Semester/Term	3rd-Year, First Semester, 2Term
Days, Periods, and Classrooms	(2T) Weds5-8：IAS K211
Lesson Style	Lecture	Lesson Style (More Details)	Face-to-face, Online (on-demand)
This is a lecture-based class, primarily held in person, with online (on demand) sessions available only in special situations, such as severe weather, the instructor’s business trip, or other exceptional circumstances. The first lesson will be conducted in person.
Credits	2.0	Class Hours/Week	4	Language of Instruction	E : English
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	The course introduces core concepts and methods of nonparametric analysis, focusing on statistical inference without parametric assumptions and practical applications to real data.
Class Schedule	lesson1 Foundations of Statistical Inference lesson2 Parametric vs Nonparametric Inference lesson3 Permutation Logic and Binomial Tests lesson4 Order Statistics, Ranks, and Data Exploration lesson5 Single Sample Inference: The Sign Test lesson6 Rank Based Inference for Medians lesson7 Scores, Robustness, and Test Comparisons lesson8 Empirical Distribution Functions and Distributional Tests lesson9 Dichotomous Data, Trend, and Randomness lesson10 Paired Samples and Matched Designs lesson11 Two Independent Samples: Location Inference lesson12 Two Independent Samples: Beyond Location lesson13 Three or More Samples lesson14 Structured Data and Survival Analysis lesson15 Correlation, Concordance, and Synthesis
Text/Reference Books,etc.	N. C. Smeeton, N. H. Spencer, and P. Sprent. Applied Nonparametric Statistical Methods, (Fifth Edition). New York, NY, USA: Chapman and Hall/CRC, 2025.
PC or AV used in Class,etc.	Text, Handouts, moodle
(More Details)
Learning techniques to be incorporated	Quizzes/ Quiz format, Post-class Report
Suggestions on Preparation and Review	1. Each lesson's materials (e.g., slides provided by the instructor) should be reviewed both before and after class. 2. The meaning of all English technical terms should be understood before class.
Requirements	Prior coursework in inferential statistics is desirable, along with a working background in either R or Python programming.
Grading Method	The grade will be based on the following two components: 1. in‑class tests, and 2. a final report.
Practical Experience
Summary of Practical Experience and Class Contents based on it
Message
Other	Please bring your own PC, as we will be doing exercises.
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.

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