700
Theory and applications of generalized linear models, models for continuous data, models for binary and polytomous data, log-linear models, quasi-likelihood functions and model checking. Prerequisite:
STA 682.
Credits
3(3-0)
Theory of point estimation in Euclidean sample spaces. Topics include unbiasedness, equivariance, global properties, large-sample theory, and asymptotic optimality. Prerequisite:
STA 684.
Credits
3(3-0)
Generating functions and inversion theorem, advanced methods for generating univariate and multivariate discrete and continuous probability distributions, distribution properties, estimation and their applications. Prerequisite:
STA 684.
Credits
3(3-0)
Teaching of approved undergraduate statistics, actuarial or data science courses. Students will conduct their teaching internship under the supervision of a graduate faculty member. CR/NC only. Prerequisite: Successful completion of all qualifying examinations.
Credits
3(Spec)
In-depth advanced practicum project supervised by a faculty member or a field supervisor in an external agency. CR/NR only. Prerequisites: Successful completion of all required Ph.D. qualifying examinations; permission of the faculty supervisor.
Credits
3(Spec)
Advanced topics that are not included in regular courses. Course may be taken for credit more than once, total credit not to exceed six hours. Prerequisites: doctoral student status; permission of instructor.
Credits
1-6(Spec)
The in-depth study of a topic in statistics under the direction of a faculty member. May be taken for credit more than once, total credit not to exceed nine hours. Prerequisites: Permission of instructor.
Credits
1-9(Spec)