|Name : Park, Mingue (박 민 규)|
|Position : Professor|
|TEL : (02) 3290-2243|
|E-mail : firstname.lastname@example.org|
|Office : 422 CPSE Bldg.|
|Research Interests : Design and estimation for sample survey. Public opinion survey.|
Ph.D. in Statistics, Iowa State University, 2002.
M.S. in Statistics, Korea University, 1996.
B.A. in Statistics, Korea University, 1994.
Professor, Department of Statistics, Korea University, March 2014 ~ Present
Assistant Professor, Department of Statistics, University of Nebraska at Lincoln, August 2002 ~ August 2006
|Books||• Sampling Design and Inference using SAS, Free Academy, 2016.7|
International Academic Journals
•Lee, I. and Park, M. (2017). “Optimal Sampling Design under the Response Homogeneous Group Response Mechanism: A Prediction Approach.” Journal of the Korean Statistical Society. Available at http://dx.doi.org/10.2016/j.jkss.2016.09.001
•Kim, G. and Park, M. (2016). “Variance estimation of DEE and regression estimator when a first-phase cluster sample is restratified.” Communications in Statistics - Simulation and Computation. Available at http://www.tandfonline.com/doi/abs/10.1080/03610918.2015.1071390
•Park, M. (2015). “Partially Calibrated Poststratified Weights for Handling Unit Nonresponse.” Communications In Statistics-Theory and Methods. 44, 2983-3000
•Park, M and Fuller, W. A. (2012). "Generalized regression estimators." Encyclopedia of Environmetrics 2nd Ed. 1162-1166, John Wiley & Sons Ltd.
•Kim, J. K. and Park, M. (2010). "Calibration estimation in survey sampling." The International Statistical Review, 78, 21-39.
•Park, M. and Fuller, W. A. (2009). "The mixed model for survey regression estimation." The Journal of Statistical Planning and Inference, 139, 1320-1331.
•Park, M. and Cho, H. (2008). "Minimum MSE regression estimator with estimated population quantities of auxiliary variables." Computational Statistics and Data Analysis, 53, 394-404.
•Park, M and Choi, B. (2008). "Bias corrected Maximum Likelihood Estimator under the generalized linear model for a binary variable." Communications in Statistics-Simulation and Computation, 37, 1507-1514.
•Park, M and Yang, M. (2008). "Ridge Regression Estimation for Survey Samples," Communications in Statistics- Theory and Methods, 37, 532-543.
•Yang, M. and Park, M. (2007). "Efficient Crossover Designs With Three Periods," The Journal of Statistical Planning and Inference, 137, 2056-2067.
•Park, M. (2006). "Alternative designs for Regression Estimation," Journal of Official Statistics, 22, 541-563.
•Park, M. and Fuller, W. A. (2005). "Towards nonnegative regression weights for survey samples," Survey Methodology, 31, 85-93.
Domestic Academic Journals
•Park, M. (2015). “Nonresponse Adjusted Raking Ratio Estimation.” Communications for statistical applications and methods. 22, 655-664.
•Lee, I. and Park, M. (2015). “A Study on Sample Allocation for Stratified Sampling.” The Korean Journal of Applied Statistics. 28, 1047-1061. (In Korean)
•Park, M. and Kim, S. (2015). A Case Study on Construction and Use of Longitudinal Weights for Korea Labor Income Panel Survey. Survey Research. 16, (1), 49-71. (In Korean)
•Kim, D. and Park, M. (2014). Comparison of Several Micro Matching Methods: Application to the Economic Activity Census and Time Use Survey. Journal of The Korean Data Analysis Society . 16, (5B), 2393-2408. (In Korean)
•Kim, G. and Park, M. (2013). A Case Study on the Sampling Design for 2012 Survey on the Employment Status of the Disabled in Business Journal of The Korean Data Analysis Society . 15, (3B), 1273-1288. (In Korean)
•Park, M., Cho, S., Song, J., Kim, O. and Jang, Y. (2012). A Study on the Model Bias of the Estimator under the Quota Sampling Design. Survey Research . 13, (2) 99-109. (In Korean)
•Park, M. (2012). Construction of Non-extreme Weighted Regression Weights for Sample Surveys. Journal of The Korean Data Analysis Society . 14, 1(A), 1-12.
•Park, M. (2012). "Construction of Non-extreme Weighted Regression Weights for Sample Surveys." Journal of The Korean Data Analysis Society, 14, (1), 1-12.
•Jang, Y., Cho, S., Song, J., Kim, O. and Park, M. (2011). Effects of Scale Size on Validity, Reliability and Easiness of Response in a Web-based Survey. Survey Research . 12, (3), 1-23. (In Korean)
•Park, M., Lee, K., Park, H. and Kang, H. (2011). A Study on the Construction of Weights for KYPS. Survey Research . 12 , (3), 173-186. (In Korean)
•Song, J. and Park, M. (2011). A Study of sample size for two-stage cluster sampling. The Korean Journal of Applied Statistics . 24, (2), 393-400. (In Korean)
•Park, J., Byun, J. and Park, M. (2010). Construction of Sampling Frames for the 5th Korea National Health and Nutrition Examination Survey. The Korean Journal of Applied Statistics 23, (5), 923-932. (In Korean)
•Song, J. and Park, M. (2010). A Study on the Construction of Weights for Combined Rolling Samples. Survey Research . 11, (1), 19-41. (In Korean)
•Kang, H., Park, S., Kim, J., Kim, I., Lee, D., Hwang, J. and Park, M. (2009). A Case Study on the Construction of the Sampling Frame and Sampling Design for 2008 Seoul Survey. Survey Research . 10, (3), 157-172. (In Korean)
•Kim, G. and Park, M. (2009). Efficient use of auxiliary information through the stratified sampling and systematic sampling design. Survey Research . 10, (1), 155-165. (In Korean)
•Park, M. (2008). Model-Based Prediction of the Population Proportion and Distribution Function Using a Logistic Regression. Communications for Statistical Applications and Methods . 15, 783-791.
•Kim. G., Park, M. and Oh, S. (2008). A study on the imputation techniques used to impute the missing values for 2007 advertising industry survey, Journal of The Korean Data Analysis Society . 10, 1483-1493.
•Park, M and Johnson, D. (2006). "Use of hot-deck imputation in estimating the population proportions," Applied Statistics, 2, 55-64.
International Academic Conference Proceeding
•Fuller, W. A. and Park, M. (2002). "Model weights for regression estimation," 2002 Proceedings of the American Statistical Association, Section on Survey Research Methods[CD-ROM], Alexandria, VA: American Statistical Association