Dr. Kleiber is an expert in spatial statistics, developing theory for multivariate space-time processes, including flexible nonstationary models as well as feasible estimation approaches for large datasets. Dr. Kleiber also has expertise in computer experiments, and has developed methodological approaches for calibrating, emulating and analyzing complex geophysical computer models. Another primary focus is statistical climatology, especially in building stochastic weather simulators for use in agricultural, ecological and hydrological modeling. He has also developed approaches for probabilistic weather forecasting, focusing on sharp and calibrated local forecasting for temperature and precipitation. Recent research has focused on stochastic parameterizations and stochastic modeling for energy applications.
keywords
spatial statistics, matrix-valued positive definite functions, covariance functions, stochastic modeling, geostatistics, Gaussian processes, computer experiments, nonstationarity, space-time processes, model calibration, model emulation, large datasets, stochastic weather generators
APPM 4540 - Introduction to Time Series
Primary Instructor
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Spring 2018
Studies basic properties, trend-based models, seasonal models modeling and forecasting with ARIMA models, spectral analysis and frequency filtration. Same as STAT 5540 and MATH 4540 and MATH 5540.
APPM 4580 - Statistical Learning
Primary Instructor
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Spring 2018 / Spring 2019
Consists of applications and methods of statistical learning. Reviews multiple linear regression and then covers classification, regularization, splines, tree-based methods, support vector machines, unsupervised learning and Gaussian process regression. Same as STAT 5610.
APPM 5540 - Introduction to Time Series
Primary Instructor
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Spring 2018
Studies basic properties, trend-based models, seasonal models modeling and forecasting with ARIMA models, spectral analysis and frequency filtration. Department enforced prerequisite: APPM 5520 or MATH 5520. Same as STAT 4540 and MATH 4540 and MATH 5540.
APPM 5580 - Introduction to Statistical Learning
Primary Instructor
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Spring 2018 / Spring 2019
Consists of applications and methods of statistical learning. Covers multiple linear regression, classification, regularization, splines, tree-based methods, support vector machines and unsupervised learning. Same as APPM 4580.
APPM 6920 - Professional Internship
Primary Instructor
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Fall 2024
This class provides a structure for Applied Mathematics graduate students to receive academic credit for internships with industry partners that have an academic component to them suitable for graduate-level work. Participation in the program will consist of an internship agreement between a student and an industry partner who will employ the student in a role that supports the academic goals of the internship. Instructor participation will include review of internship agreement, facilitation of mid-term and final assessments of student performance, and support for any academic-related issues that may arise during the internship period. May be repeated up to 6 total credit hours.
APPM 6950 - Master's Thesis
Primary Instructor
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Fall 2019 / Spring 2020 / Fall 2020 / Spring 2021 / Fall 2021 / Spring 2022 / Fall 2024
May be repeated up to 6 total credit hours.
APPM 7400 - Topics in Applied Mathematics
Primary Instructor
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Spring 2021 / Spring 2022 / Spring 2023
Provides a vehicle for the development and presentation of new topics with the potential of being incorporated into the core courses in applied mathematics. May be repeated up to 6 total credit hours.
MATH 4540 - Introduction to Time Series
Primary Instructor
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Spring 2018
Studies basic properties, trend-based models, seasonal models, modeling and forecasting with ARIMA models, spectral analysis and frequency filtration. Same as MATH 5540 and STAT 4540 and STAT 5540.
MATH 5540 - Introduction to Time Series
Primary Instructor
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Spring 2018
Studies basic properties, trend-based models, seasonal models, modeling and forecasting with ARIMA models, spectral analysis and frequency filtration. Department enforced prerequisite: MATH 4520 or MATH 5520 or APPM 4520 or APPM 5520. Same as MATH 4540 and STAT 4540 and STAT 5540.
STAT 4430 - Spatial Statistics
Primary Instructor
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Spring 2019 / Fall 2020 / Fall 2021 / Spring 2023 / Spring 2024
Introduces the theory of spatial statistics with applications. Topics include basic theory for continuous stochastic processes, spatial prediction and kriging, simulation, geostatistical methods, likelihood and Bayesian approaches, spectral methods and an overview of modern topics such as nonstationary models, hierarchical modeling, multivariate processes, methods for large datasets and connections to splines. Recommended prerequisites: STAT 4520 OR STAT 5520 OR MATH 4520 OR MATH 5520. Same as STAT 5430.
STAT 4610 - Statistical Learning
Primary Instructor
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Fall 2020 / Fall 2021 / Fall 2023 / Spring 2024
Consists of applications and methods of statistical learning. Reviews multiple linear regression and then covers classification, regularization, splines, tree-based methods, support vector machines, unsupervised learning and Gaussian process regression. Same as STAT 5610.
STAT 5430 - Spatial Statistics
Primary Instructor
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Spring 2019 / Fall 2020 / Fall 2021 / Spring 2023 / Spring 2024
Introduces the theory of spatial statistics with applications. Topics include basic theory for continuous stochastic processes, spatial prediction and kriging, simulation, geostatistical methods, likelihood and Bayesian approaches, spectral methods and an overview of modern topics such as nonstationary models, hierarchical modeling, multivariate processes, methods for large datasets and connections to splines. Recommended prerequisite: previous coursework equivalent to one of STAT 3400 or STAT 4010 or STAT 5010 and one of STAT 4520 or STAT 5520 or STAT 5530; all with a minimum grade of C-. Same as STAT 4430.
STAT 5530 - Mathematical Statistics
Primary Instructor
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Fall 2024
Covers the theory of estimation, confidence intervals, hypothesis testing, and decision theory. In particular, it covers the material of APPM 5520 in greater depth, especially the topics of optimality and asymptotic approximation. Additional topics include M-estimation, minimax tests, the EM algorithm, and an introduction to Bayesian estimation and empirical likelihood techniques. Recommended Prerequisite is a one-semester calculus-based probability course such as MATH 4510 or APPM 3570. Degree credit not granted for this course and STAT 5520 or MATH 5520 or STAT 4520 or MATH 4520.
STAT 5610 - Statistical Learning
Primary Instructor
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Fall 2020 / Fall 2021 / Fall 2023 / Spring 2024
Consists of applications and methods of statistical learning. Reviews multiple linear regression and then covers classification, regularization, splines, tree-based methods, support vector machines, unsupervised learning and Gaussian process regression. Recommended prerequisite: previous coursework equivalent to that of STAT 3400 or STAT 4010 or STAT 5010; minimum C- grade for all. Same as STAT 4610.
