Syllabus.  
Stat 222, Geog m205, UP m215. Spatial Statistics. <br>
Prof. Rick Paik Schoenberg. <br>

Spring 2021. <br><br>

Lectures:  TTh 2pm-315pm. Online via zoom with meeting ID 918 499 4904. The password for week 1 is 142196. It will change in future lectures and I will announce the password in class during week 1. 
<a href = "https://ucla.zoom.us/j/9184994904"> https://ucla.zoom.us/j/9184994904 </a> . 
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Contact instructor by email for the password if needed. 
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Office Hours:  Thu 130-2pm, same website as the lectures.<br><br>

Email:  frederic@stat.ucla.edu<br><br>

Course webpage:  <a href="http://www.stat.ucla.edu/~frederic/222/S21">
http://www.stat.ucla.edu/~frederic/222/S21 . </a> 
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I am not maintaining the CCLE site. See the site above for course information. </a><br><br>

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.<br><br>
  
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.<br><br> 

Grading: <br>
exam (45%) Thu May 20 2-315pm. <br>
Oral Presentation (10%) -- via zoom, last 2 weeks of class.<br>
Written project (45%) -- due Wed Jun9, 8pm, by email to me. <br><br> 

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.
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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. 
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Written Project:<br>
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.<br><br>

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).
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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. 
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