Syllabus.
Stat 222, Geog m205, UP m215. Spatial Statistics.
Prof. Rick Paik Schoenberg.
Spring 2018.
Lectures: MW 930-1045am, Geology 4660.
Office Hours: Wed 11am-11:55am, MS 8965.
Email: frederic@stat.ucla.edu
Course webpage:
http://www.stat.ucla.edu/~frederic/222/S18 .
I am not maintaining the CCLE site. See the site above for 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 May 21, in class.
Oral Presentation (10%) -- last 2 weeks of class.
Written project (45%) -- due Sat, Jun9, 8pm, by email to me.
NO CLASS WED APR 4 or MON APR 9.
Attendance in class is generally not mandatory and not counted as
part of the grade. However, the last weeks of class is an exception:
all students MUST attend class, on time, for the final two weeks.
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 on the notes 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 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 2 weeks of
class. These will involve simply presenting a clear and
concise 5 minute 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! You should only show around 4-6 figures in your
presentation. Email me a pdf of your slides the night before your presentation, by 8pm, so I can have them ready before class starts.