Shemyakin, Arkady portrait

Shemyakin, Arkady

Professor
Degree
Ph.D., Russian Academy of Sciences-Siberia
Office
OSS 221
Phone
(651) 962-5522
Toll Free
(800) 328-6819, Ext. 2-5522
Fax
651-962-5670
Mail
OSS 201

Arkady Shemyakin did his graduate study at Novosibirsk State University and post-graduate study at the Sobolev Institute of Mathematics in Novosibirsk, Russia. His main research interests lie in the field of Bayesian Statistics, non-informative priors, and copula analysis with the emphasis on applications to Insurance and Finance.

Dr. Shemyakin has taught full-time at Novosibirsk State University and as a visiting professor at several schools including Astrakhan State University in Russia, University of Minnesota and Heriot-Watt University in Edinburgh, UK.

Arkady is the current President of American Siberian Education Foundation and the past President of the Twin Cities chapter of American Statistical Association. He serves on the editorial boards and as a reviewer for several statistical journals. Besides that, he tries to spend as much time as possible playing chess, tennis, and skiing.

Fall 2016 Courses

Fall 2016 Courses
Course - Section Title Days Time Location
MATH 303 - 01 Statistics/Applied Sciences M - W - - - - 1525 - 1700 OSS 214
CRN: 42534 4 Credit Hours Instructor: Arkady Shemyakin Probability, Estimation, Hypothesis Testing, Analysis of Variance, Regression Analysis, Topics selected from Experimental Design, Statistical Process Control, Non-Parametric Methods, Factor Analysis as time permits. Offered Fall of even-numbered years. Prerequisite: A grade of C- or above in MATH 200 NOTE: Students who receive credit for MATH 303 may not receive credit for MATH 313 or STAT 314.

Schedule Details

Location Time Day(s)
STAT 314 - 01 Mathematical Statistics - T - R - - - 1330 - 1510 OSS 214
CRN: 41002 4 Credit Hours Instructor: Arkady Shemyakin Populations and random sampling; sampling distributions. Theory of statistical estimation; criteria and methods of point and interval estimation. Theory of testing statistical hypotheses; non-parametric methods. Offered in fall semester. Prerequisite: MATH 240 and 313 NOTE: Students who recieve credit for MATH 314 may not receive credit for MATH 303.

Schedule Details

Location Time Day(s)
STAT 314 - 02 Mathematical Statistics - T - R - - - 1525 - 1700 OSS 214
CRN: 41321 4 Credit Hours Instructor: Arkady Shemyakin Populations and random sampling; sampling distributions. Theory of statistical estimation; criteria and methods of point and interval estimation. Theory of testing statistical hypotheses; non-parametric methods. Offered in fall semester. Prerequisite: MATH 240 and 313 NOTE: Students who recieve credit for MATH 314 may not receive credit for MATH 303.

Schedule Details

Location Time Day(s)

J-Term 2017 Courses

J-Term 2017 Courses
Course - Section Title Days Time Location

Spring 2017 Courses

Spring 2017 Courses
Course - Section Title Days Time Location
STAT 333 - D01 Applied Statistical Methods - T - R - - - 1330 - 1510 OSS 226
CRN: 20773 4 Credit Hours Instructor: Arkady Shemyakin Regression and exponential smoothing methods; Stochastic Time Series: auto- and cross-correlation, autoregressive moving average models; application to forecasting. Prerequisites: MATH 303 or 314 or STAT 314 or permission of instructor

Schedule Details

Location Time Day(s)
STAT 333 - D02 Applied Statistical Methods - T - R - - - 1525 - 1700 OSS 226
CRN: 21247 4 Credit Hours Instructor: Arkady Shemyakin Regression and exponential smoothing methods; Stochastic Time Series: auto- and cross-correlation, autoregressive moving average models; application to forecasting. Prerequisites: MATH 303 or 314 or STAT 314 or permission of instructor

Schedule Details

Location Time Day(s)
STAT 370 - 01 Bayesian Models M - W - - - - 1525 - 1700 OSS 214
CRN: 21542 4 Credit Hours Instructor: Arkady Shemyakin The course covers a range of statistical models used in applications including: Actuarial Science, Finance, Health and Social Sciences. It is oriented towards practical model construction and problem sovling. Review of parametric statistical models and principles of statistical inference. Application to loss and ruin models. Construction of empirical and parametric models and model selection. Credibility theory. Simulation. Offered every other year. Prerequisite: MATH 313 and STAT 314 or STAT 220 and STAT 320

Schedule Details

Location Time Day(s)