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Kleiber, William Paul

Associate Professor

Positions

Research Areas research areas

Research

research overview

  • 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

Publications

selected publications

Teaching

courses taught

  • APPM 4540 - Introduction to Time Series
    Primary Instructor - 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 - 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 - 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 - 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 6950 - Master's Thesis
    Primary Instructor - Fall 2019 / Spring 2020 / Fall 2020 / Spring 2021 / Fall 2021 / Spring 2022
    May be repeated up to 6 total credit hours.
  • APPM 7400 - Topics in Applied Mathematics
    Primary Instructor - 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 - 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 - 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 - 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 spines. Same as STAT 5430.
  • STAT 4610 - Statistical Learning
    Primary Instructor - 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 - 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 spines. Same as STAT 4430.
  • STAT 5610 - Statistical Learning
    Primary Instructor - 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 4610.

Background

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