Syllabus.
Stat 222, Geog m205, UP m215. Spatial Statistics.
Prof. Rick Paik Schoenberg.
Fall 2023.
Lectures: MW 1230pm-1:45pm in PubAff2232.
NO LECTURE MON NOV6, for the faculty retreat!
Office hours: Thu 1050-1130am, 3873 Slichter.
Email: frederic@stat.ucla.edu
Course webpage:
http://www.stat.ucla.edu/~frederic/222/F23 .
I am not maintaining the CCLE or Canvas sites.
See the site above for course information.
Statistics 222 will explore the methods used in the analysis of
spatial-temporal data, especially point process data.
The course will be both theoretical and
applied. Students will learn standard concepts in modeling and parameter estimation,
followed by some more recent topics such as point process residual analysis and
nonparametric estimation of triggering functions in Hawkes models. Examples will be provided throughout the instruction of the course, and
students will implement the techniques discussed in class, using real data
sets. Dedicated students should come away with an in-depth understanding
of statistical concepts related to spatial-temporal point processes, as well as a thorough
comprehension of how and when to implement various techniques in practice.
The course is designed for graduate students in statistics or
mathematics, and may also be taken by students from other disciplines
provided those students have sufficient mathematical and statistical
backgrounds.
Some experience in statistical computing with R is recommended.
Grading:
exam (45%) Mon Nov 20 in class.
Oral Presentation (10%) -- last 2 weeks of class.
Written project (45%) -- due Tue Dec12 11:59pm by email to me.
Attendance Nov27, Nov29, Dec4 and Dec6 is mandatory for all students, for the oral reports.
Attendance for lectures, on all other days, is generally not mandatory and not
counted as part of the grade.
However, if you cannot attend, please contact another student to find out what you missed rather than asking me to fill you in.
Late homeworks will not be accepted at all.
There will be no extensions for the project or presentation.
Students who are unable to make these dates or otherwise fulfill
the course requirements must consult with the instructor in advance, if possible.
Students with learning disabilities must consult with the instructor by the 2nd
week of class if special arrangements are required.
Written Project: due Tue Dec12 11:59pm, by email to frederic@stat.ucla.edu.
Oral presentations: Nov27, Nov29, Dec4, and Dec6.
There will most likely
be no extensions for the project or presentation.
Students who are unable to make these dates or otherwise
fulfill the course requirements
must consult with the instructor in advance, if possible.
I am almost always open to questions, but one question I would like you not to ask me is "What did we do in class?" If you miss class, please get caught up from a fellow classmate.
Written Project:
Find a spatial-temporal point process dataset and analyze it using some of the relevant methods
described in class. Your report should contain about 4-6 pages
of text, followed by as many figures as appropriate. You may include as
many figures as you like, but the text itself should not exceed 6 pages, double spaced.
In selecting your dataset,
choose something that interests you, and try to choose a dataset where the quantification of clustering or inhibition of the locations of the points would be of interest.
Begin your paper with an introduction, a description of your data and how
they were obtained, and a summary of the main question(s) to be addressed
in your paper. Then summarize your analyses, paying special attention
to any assumptions you are making and the plausibility of those assumptions.
Conclude by assessing how effective the methods you used were in helping
to answer your main question(s).
Oral presentations of project results will take place on the last few days of
class. These will involve simply presenting a clear and
concise summary of your dataset including a couple of your main results.
Do not try to show all the results from your paper in your oral
presentation! I will talk more about the presentations as we get closer to the date.