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Kai-Sheng Song

Title: Professor

Department: Mathematics

College: College of Science

Curriculum Vitae

Curriculum Vitae Link

Education

  • PhD, University of California at Davis, 1993
    Major: Statistics
  • MS, University of California at Davis, 1990
    Major: Statistics
  • BS, Northeast Normal University, 1983
    Major: Mathematics

Current Scheduled Teaching

MATH 6950.727Doctoral DissertationSpring 2025
MATH 4610.001ProbabilitySpring 2025
MATH 5820.001Probability and StatisticsSpring 2025
MATH 6950.703Doctoral DissertationFall 2024
MATH 4610.001ProbabilityFall 2024 Syllabus
MATH 5810.001Probability and StatisticsFall 2024
MATH 5900.703Special ProblemsFall 2024
MATH 5900.720Special ProblemsFall 2024

Previous Scheduled Teaching

MATH 6950.701Doctoral DissertationSummer 5W2 2024
MATH 6950.724Doctoral DissertationSpring 2024
MATH 6950.728Doctoral DissertationSpring 2024
MATH 5820.001Probability and StatisticsSpring 2024 SPOT
MATH 5900.722Special ProblemsSpring 2024
MATH 5900.727Special ProblemsSpring 2024
MATH 6900.711Special ProblemsSpring 2024
MATH 4650.001StatisticsSpring 2024 Syllabus SPOT
MATH 6950.702Doctoral DissertationFall 2023
MATH 5810.001Probability and StatisticsFall 2023 SPOT
MATH 5900.714Special ProblemsFall 2023
MATH 5900.725Special ProblemsFall 2023
MATH 6900.003Special ProblemsFall 2023
MATH 6950.708Doctoral DissertationSpring 2023
MATH 5820.001Probability and StatisticsSpring 2023 SPOT
MATH 6900.703Special ProblemsSpring 2023
MATH 6900.704Special ProblemsSpring 2023
MATH 6900.706Special ProblemsSpring 2023
MATH 4650.001StatisticsSpring 2023 Syllabus SPOT
MATH 6950.702Doctoral DissertationFall 2022
MATH 5810.001Probability and StatisticsFall 2022 SPOT
MATH 5810.601Probability and StatisticsFall 2022 SPOT
MATH 5900.710Special ProblemsFall 2022
MATH 5900.702Special ProblemsSummer 10W 2022
MATH 6950.702Doctoral DissertationSpring 2022
MATH 5820.001Probability and StatisticsSpring 2022 SPOT
MATH 6900.703Special ProblemsSpring 2022
MATH 6900.704Special ProblemsSpring 2022
MATH 4650.001StatisticsSpring 2022 Syllabus SPOT
MATH 6950.703Doctoral DissertationFall 2021
MATH 4610.001ProbabilityFall 2021 Syllabus SPOT
MATH 5810.001Probability and StatisticsFall 2021 SPOT
MATH 6950.702Doctoral DissertationSummer 10W 2021
MATH 6950.702Doctoral DissertationSummer 5W2 2021
MATH 6950.703Doctoral DissertationSummer 5W1 2021
MATH 6950.721Doctoral DissertationSpring 2021
MATH 4650.001StatisticsSpring 2021 Syllabus SPOT
MATH 6820.001Topics in StatisticsSpring 2021 SPOT
MATH 6950.722Doctoral DissertationFall 2020
MATH 6950.721Doctoral DissertationSpring 2020
MATH 5820.001Probability and StatisticsSpring 2020
MATH 4650.001StatisticsSpring 2020 Syllabus
MATH 3680.002Applied StatisticsFall 2019 Syllabus SPOT
MATH 6950.703Doctoral DissertationFall 2019
MATH 6950.722Doctoral DissertationFall 2019
MATH 5810.001Probability and StatisticsFall 2019 SPOT
MATH 6900.716Special ProblemsFall 2019
MATH 6900.722Special ProblemsFall 2019
MATH 6950.702Doctoral DissertationSummer 5W1 2019
MATH 6950.704Doctoral DissertationSummer 5W2 2019
MATH 3680.002Applied StatisticsSpring 2019 Syllabus SPOT
MATH 6950.724Doctoral DissertationSpring 2019
MATH 5900.702Special ProblemsSpring 2019
MATH 6900.703Special ProblemsSpring 2019
MATH 4650.001StatisticsSpring 2019 Syllabus SPOT
MATH 3680.002Applied StatisticsFall 2018 Syllabus SPOT
MATH 6950.703Doctoral DissertationFall 2018
MATH 6950.722Doctoral DissertationFall 2018
MATH 4610.001ProbabilityFall 2018 Syllabus SPOT
MATH 5900.722Special ProblemsFall 2018
MATH 6900.722Special ProblemsFall 2018
MATH 3680.002Applied StatisticsSpring 2018 Syllabus SPOT
MATH 6950.724Doctoral DissertationSpring 2018
MATH 5900.710Special ProblemsSpring 2018
MATH 6900.702Special ProblemsSpring 2018
MATH 4650.001StatisticsSpring 2018 Syllabus SPOT
MATH 3680.001Applied StatisticsFall 2017 Syllabus SPOT
MATH 6950.703Doctoral DissertationFall 2017
MATH 6950.722Doctoral DissertationFall 2017
MATH 6810.001Probability.Fall 2017 SPOT
MATH 5900.722Special ProblemsFall 2017
MATH 6900.722Special ProblemsFall 2017
MATH 6950.702Doctoral DissertationSummer 5W1 2017
MATH 6950.724Doctoral DissertationSpring 2017
MATH 5820.001Probability and StatisticsSpring 2017 SPOT
MATH 6900.706Special ProblemsSpring 2017
MATH 6900.724Special ProblemsSpring 2017
MATH 6900.727Special ProblemsSpring 2017
MATH 6900.728Special ProblemsSpring 2017
MATH 4650.001StatisticsSpring 2017 Syllabus SPOT
MATH 6950.722Doctoral DissertationFall 2016
MATH 4610.001ProbabilityFall 2016 Syllabus SPOT
MATH 5810.001Probability and StatisticsFall 2016 SPOT
MATH 5900.722Special ProblemsFall 2016
MATH 6900.722Special ProblemsFall 2016
MATH 6900.702Special ProblemsSummer 5W1 2016
MATH 6950.724Doctoral DissertationSpring 2016
MATH 5820.001Probability and StatisticsSpring 2016 SPOT
MATH 6900.724Special ProblemsSpring 2016
MATH 4650.001StatisticsSpring 2016 Syllabus SPOT
MATH 6950.723Doctoral DissertationFall 2015
MATH 4610.001ProbabilityFall 2015 Syllabus SPOT
MATH 6810.001Probability.Fall 2015 SPOT
MATH 5900.722Special ProblemsFall 2015
MATH 6900.722Special ProblemsFall 2015
MATH 6910.702Special ProblemsFall 2015
MATH 5900.001Special ProblemsSummer SUM 2015
MATH 6950.724Doctoral DissertationSpring 2015
MATH 5820.001Probability and StatisticsSpring 2015
MATH 5900.724Special ProblemsSpring 2015
MATH 6900.724Special ProblemsSpring 2015
MATH 6910.724Special ProblemsSpring 2015
MATH 4650.001StatisticsSpring 2015 Syllabus
MATH 6950.723Doctoral DissertationFall 2014
MATH 4610.002ProbabilityFall 2014 Syllabus
MATH 5810.001Probability and StatisticsFall 2014
MATH 5900.710Special ProblemsFall 2014
MATH 5910.703Special ProblemsFall 2014
MATH 6900.771Special ProblemsFall 2014
MATH 5820.001Probability and StatisticsSpring 2014
MATH 5900.724Special ProblemsSpring 2014
MATH 4650.001StatisticsSpring 2014 Syllabus
MATH 6950.723Doctoral DissertationFall 2013
MATH 5900.710Special ProblemsFall 2013
MATH 5910.703Special ProblemsFall 2013
MATH 6950.702Doctoral DissertationSummer 5W2 2013
MATH 6950.711Doctoral DissertationSpring 2013
MATH 5820.001Probability and StatisticsSpring 2013
MATH 4650.001StatisticsSpring 2013 Syllabus
MATH 6950.723Doctoral DissertationFall 2012
MATH 4610.001ProbabilityFall 2012 Syllabus
MATH 5810.001Probability and StatisticsFall 2012
MATH 6950.711Doctoral DissertationSpring 2012
MATH 5950.704Master's ThesisSpring 2012
MATH 5820.001Probability and StatisticsSpring 2012
MATH 5900.716Special ProblemsSpring 2012
MATH 5910.702Special ProblemsSpring 2012
MATH 4650.001StatisticsSpring 2012 Syllabus
MATH 3680.002Applied StatisticsFall 2011 Syllabus
MATH 6950.723Doctoral DissertationFall 2011
MATH 5810.001Probability and StatisticsFall 2011
MATH 5900.710Special ProblemsFall 2011
MATH 5910.703Special ProblemsFall 2011
MATH 6950.711Doctoral DissertationSpring 2011
MATH 5820.001Probability and StatisticsSpring 2011
MATH 5900.716Special ProblemsSpring 2011
MATH 6900.704Special ProblemsSpring 2011
MATH 6910.702Special ProblemsSpring 2011
MATH 4650.001StatisticsSpring 2011 Syllabus
MATH 3680.003Applied StatisticsFall 2010 Syllabus
MATH 6950.723Doctoral DissertationFall 2010
MATH 5810.001Probability and StatisticsFall 2010
MATH 6900.771Special ProblemsFall 2010
MATH 3680.002Applied StatisticsSpring 2010
MATH 6950.718Doctoral DissertationSpring 2010
MATH 5950.722Master's ThesisSpring 2010
MATH 5820.001Probability and StatisticsSpring 2010
MATH 6900.773Special ProblemsSpring 2010
MATH 4650.001StatisticsSpring 2010
MATH 3680.002Applied StatisticsFall 2009
MATH 4610.001ProbabilityFall 2009
MATH 5810.001Probability and StatisticsFall 2009
MATH 5900.710Special ProblemsFall 2009
MATH 6900.771Special ProblemsFall 2009
MATH 6900.771Special ProblemsSummer 5W2 2009
MATH 5820.001Probability and StatisticsSpring 2009
MATH 6900.773Special ProblemsSpring 2009
MATH 4650.001StatisticsSpring 2009
MATH 3680.002Applied StatisticsFall 2008
MATH 6900.774Special ProblemsFall 2008
MATH 1780.002Probability ModelsSpring 2008
MATH 5900.772Special ProblemsSpring 2008
MATH 2730.002Multivariable CalculusFall 2007
MATH 1780.001Probability ModelsFall 2007
MATH 5900.771Special ProblemsFall 2007

Published Intellectual Contributions

    Conference Proceeding

  • Song, K. (1998). K.-S. Song: On Compound Exponential Distribution. Proceedings of the 13th international Workshop on Statistical Modeling, 490-493, 1998..
  • Song, K. (1998). K.-S. Song: Recent Developments in Population Pharmacokinetic Modeling. Invited Papers of the XIXth International Biometric Conference, 197-204, 1998..
  • Journal Article

  • Song, K. (2022). Yujie Yan and Kai-Sheng Song, "A general optimal approach to Bühlmann credibility theory". Insurance: Mathematics & Economics. 104 (May) 262-282.
  • Song, K. (2021). Simultaneous Statistical Modelling of Excess Zeros, Over/underdispersion, and Multimodality with Applications in Hotel Industry. Journal of Applied Statistics. 48 (9) 1603-1627.
  • Song, K. (2017). D. Taylor, K.S. Kelly, M.L. Kohut, and K.S. Song, "Is Insomnia a Risk Factor for Decreased In uenza Vaccine Response?", Behavioral Sleep Medicine, 270-287, 2017. Behavioral Sleep Medicine. 15 (4) 270-287.
  • Taylor, D.J., Kelly, K.S., Kohut, M., Song, K. (2017). Is Insomnia as a Risk Factor for Decreased Influenza Vaccine Response?. Behavioral Sleep Medicine. 15 (4) 270-287.
  • Song, K. (2017). K.-S. Song and R. Kieschnick, "Statistical Analysis of Financial Data with Many Zeros", Invited Focus Article, WIREs Computational Statistics , 9, 1-12, 2017..
  • Song, K. (2014). D. Taylor, K.S. Kelly, M.L. Kohut, and K.S. Song, "Insomnia as a Risk Factor for Decreased Antibody Response to the In uenza Vaccine", PSYCHOSOMATIC MEDICINE, 76, A55- A56, 2014..
  • Song, K. (2013). Kai-Sheng Song: Asymptotic Relative Efficiency and Exact Variance Stabilizing Transformation for the Generalized Gaussian stribution IEEE Transactions on Information Theory, 59, 4389-4396, 2013..
  • Song, K. (2011). K.S. Song and T.H. Li: Asymptotics of Least Squares for Nonlinear Harmonic Regression. Statistics: A Journal of Theoretical and Applied Statistics, 1029-4910, 2010..
  • Song, K. (2011). K.S. Song: Max-Min Bernstein Polynomial Estimation of a Discontinuity in Distribution . Nonparametric Statistical Methods and Related Topics: Festschrift in Honor of Professor P.K. Bhattacharya on the Occasion of his 80th Birthday, Jiang, J., Roussas, G.G. and Samaniego, F.J., Eds., World Scientific..
  • Song, K. (2009). T.-H. Li and K.-S. Song: Estimation of the Parameters of Sinusoidal Signals in Non-Gaussian Noise. IEEE Transactions on Signal Processing, 57, 62-72, 2009..
  • Song, K. (2008). K.-S. Song: Globally Convergent Algorithms for Estimating Generalized Gamma Distributions in Fast Signal and Image Processing. IEEE Transactions on Image Processing, 17, 1233-1250,2008.
  • Song, K. (2008). T.-H. Li and K.-S. Song: On Asymptotic Normality of Nonlinear Least Squares for Sinusoidal Parameter Estimation. IEEE Transactions on Signal Processing, 56, 4511-4515, 2008..
  • Song, K. (2007). K.-S. Song: Fast and Globally Convergent Statistical Algorithms for Biomedical Imaging. Book of Short Papers of the Fifth S. Co. Conference on Complex Models and Computational Intensive Methods for Estimation and Prediction, 469-474, 2007..
  • Song, K. (2007). T.-H. Li and K.-S. Song: Estimation of the Frequency of Sinusoidal Signals in Laplace Noise. Proceedings of The IEEE International Symposium on Information Theory (ISIT), 1786-1790, 2007..
  • Song, K. (2006). K.-S. Song and T.-H. Li: On Convergence and Bias Correction of a Joint Estimation Algorithm for Multiple Sinusoidal Frequencies. The Journal of the American Statistical Association, Vol. 101, No. 474, 830-842 (2006)..
  • Song, K. (2006). K.-S. Song: A Globally Convergent and Consistent Method for Estimating the Shape Parameter of a Generalized Gaussian Distribution. IEEE Transactions on Information Theory, 52, 510-527 (2006).
  • Song, K. (2006). T.-H. Li and K.-S. Song: A Joint Estimation Algorithm for Multiple Sinusoidal Frequencies. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)}, 3, III-508-III-511, 2006..
  • Song, K. (2005). K.-S. Song: Blind Efficient Scores Detection and Decoding of Multi-bit Watermarks. Proceedings of the SPIE Conference on ``Mathematics of Data/Image Coding, Compression, and Encryption VIII, with Applications, 5915, 591507, 1-10, 2005..
  • Song, K. (2003). M. Hollander, G. Laird and K.-S. Song: Nonparametric Inference for the Proportionality Function in the Random Censorship Model. Journal of Nonparametric Statistics, 15, 151-169, 2003..
  • Song, K. (2002). H. Xiao, C. Campbell and K.-S. Song: A Trend Analysis of Organ Transplantation among Racial or Ethnic Groups. Journal of National Medical Association, 94, 15-20, 2002..
  • Song, K. (2002). J. Salzman, J.B. Ruhl and K.-S. Song: Regulatory Traffic Jams. Wyoming Law Review, 2, 253-289, 2002.
  • Song, K. (2002). J.B. Ruhl, J. Salzman, K.-S Song and H. Yu: Environmental Compliance: Another Integrity Crisis or Too Many Rules? Natural Resources & Environment , 17, 24-29, 2002..
  • Song, K. (2002). K.-S. Song: Goodness-of-Fit Tests Based on Kullback-Leibler Discrimination Information. IEEE Transactions on Information Theory, 48, 1103-1117, 2002..
  • Song, K. (2002). T.-H. Li and K.-S. Song: Asymptotic Analysis of a Fast Algorithm for Efficient Multiple Frequency Estimation. IEEE Transactions on Information Theory , 48, 2709-2720 , 2002..
  • Song, K. (2001). K.-S. Song: Renyi Information, Loglikelihood and an Intrinsic Distribution Measure. Journal of Statistical Planning and Inference, 93, 51-69, 2001..
  • Song, K. (2001). M. Hollander, G. Laird and K.-S. Song: Maximum Likelihood Estimation in the Proportional Hazards Model of Random Censorship. Statistics, 35, 245-258, 2001..
  • Song, K. (2000). K.-S. Song and T.-H. Li: A Statistically and Computationally Efficient Method for Frequency Estimation. Stochastic Processes and Their Applications, 86, 29-47, 2000..
  • Song, K. (2000). K.-S. Song: Limit Theorems for Nonparametric Sample Entropy Estimators. Statistics and Probability Letters, 49, 9-18, 2000..
  • Song, K. (1997). H.G. Muller and K.-S. Song: Two-Stage Change-Point Estimators in Smooth Regression Models. Statistics and Probability Letters, 34, 323-335, 1997..
  • Song, K. (1996). H.G. Muller and K.-S. Song: A Set-Indexed Process in a Two-Region Image. Stochastic Processes and Their Applications, 62, 87-101, 1996..
  • Song, K. (1995). H. Rubin and K.-S. Song: Exact Computation of the Asymptotic Efficiency of Maximum Likelihood Estimators of Discontinuous Signal in a Gaussian White Noise. The Annals of Statistics, 23, 732-739, 1995..
  • Song, K. (1995). K.-S. Song, H.G. Muller, A.J. Clifford, H.C. Furr, and J.A. Olson: Estimating Derivatives of Pharmacokinetic Response Curves with Varying Bandwidths. Biometrics, 51, 12-20, 1995..
  • Song, K. (1994). H.G. Muller and K.-S. Song: Cube Splitting in Multidimensional Edge Estimation. Change-point Problems, IMS Lecture Notes-Monograph Series, 23, 210-223, 1994..
  • Song, K. (1994). H.G. Muller and K.-S. Song: Maximin Estimation of Multidimensional Boundaries. Journal of Multivariate Analysis, 50, 265-281, 1994..
  • Song, K. (1993). H.G. Muller and K.-S. Song: Identity Reproducing Multivariate Nonparametric Regression. Journal of Multivariate Analysis, 46, 237-253, 1993..
  • Song, K. (1985). R. Z. Chen, H. Gao, and K.-S. Song: Existence and Uniqueness of Regular Solutions of a Nonstationary Population Evolution Equation with a Migration Term. Journal of Northeast Normal University, 3, 1-6, 1985..

Contracts, Grants and Sponsored Research

    Grant - Research

  • Song, K. (Supporting), "Is Insomnia a Risk Factor for Decreased In uenza (e.g. H1N1) Vaccine Response?," sponsored by NIAID, NIH, Federal, $442838 Funded. (2011 - 2014).
,
Overall
Summative Rating
Challenge and
Engagement Index
Response Rate

out of 5

out of 7
%
of
students responded
  • Overall Summative Rating (median):
    This rating represents the combined responses of students to the four global summative items and is presented to provide an overall index of the class’s quality. Overall summative statements include the following (response options include a Likert scale ranging from 5 = Excellent, 3 = Good, and 1= Very poor):
    • The course as a whole was
    • The course content was
    • The instructor’s contribution to the course was
    • The instructor’s effectiveness in teaching the subject matter was
  • Challenge and Engagement Index:
    This rating combines student responses to several SPOT items relating to how academically challenging students found the course to be and how engaged they were. Challenge and Engagement Index items include the following (response options include a Likert scale ranging from 7 = Much higher, 4 = Average, and 1 = Much lower):
    • Do you expect your grade in this course to be
    • The intellectual challenge presented was
    • The amount of effort you put into this course was
    • The amount of effort to succeed in this course was
    • Your involvement in course (doing assignments, attending classes, etc.) was
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