▶ STATISTICAL METHODS FOR LINEAR MODELS (STA504) 선형모형방법론
This course covers the theory and application of the generalized linear model including elementary theories of mathematical statistics about linear models, geometrical interpretation, calculation of least squares, analysis of variance, and regression analysis.
▶ MULTIVARIATE STATISTICAL METHODS (STA505) 다변량통계방법론
This course covers multivariate linear models, correlation structure analysis, discriminant and classification analysis, multidimensional scaling, and graphic techniques from the viewpoints of data analysis.
▶ NON-PARAMETRIC STATISTICAL METHODS (STA508) 비모수통계방법론
This course examines the application of classical non-parametric statistical methods and problems of estimation, comparison with parametric methods in efficiency, one- and two-way classification of order statistics, and the investigation of distribution-free regression models from an applied perspective.
▶ STATISTICAL COMPUTING METHODS (STA509) 통계계산방법론
This course covers the calculation errors of floating points, numerical analysis method, percentile calculation, use and development of statistical software, generating random numbers, testing and application simulations, matrix algebra, optimization problems and statistical graphics.
▶ BAYESIAN STATISTICS (STA510) 베이즈 통계학
This course covers the basics of Bayesian statistics with an emphasis on understanding concepts and applications. Topics include prior and posterior distribution, Bayesian inference and hypothesis testing, and Bayesian approaches to linear models.
▶ FINANCIAL TIME SERIES ANALYSIS (STA511) 금융시계열분석
Through time series analysis of financial assets, the course covers statistical and econometric models that estimate the future values of investment assets and measure future risks; students will practice application methods. For the estimation of future values, ARIMA, transfer function-noise model analysis, intervention analysis, and Kalman filter techniques will be studied, and for the measurement of future risks, GARCH, IGARCH, EGARCH, and GARCH-M models will be studied. Students will also learn portfolio theory, the capital assets pricing model, option pricing, and VaR.
▶ PROBABILITY AND RANDOM PROCESS (STA512) 확률및확률과정론
This course covers abstract probability space, probability distribution, the classification and relations of stochastic convergence, limit theorems, the Markov process, Brownian motion, and the Martingale theory.
▶ STATISTICAL INFERENCE (STA513) 추론통계학
* Students who have already registered "MATHEMATICAL STATISTICS (STAT 515)" are not allowed to register this class, and will not get as a major credit.
This course covers theories of statistical inference. Students will study concepts and theoretical background of sample distribution, estimation and testing, and introduce variance/regression analysis, nonparametric inference, and Bayesian inference.
▶ STATISTICAL METHODS FOR CATEGORICAL DATA ANALYSIS (STA514) 범주형자료분석방법론
This course covers theory and application for the analysis of contingency tables, the analysis of categorical data by generalized linear models including logit and log-linear models, and the analysis of repeated categorical data using GEE and random effects.
▶ STATISTICS SEMINAR (STA516) 논문세미나
Students will learn the basic principles for thesis writing. They will also have a chance to write a short thesis.
▶ EXPERIMENTAL DESIGN (STA603) 실험계획법
This course covers fundamental design methods including factorial experimental design, fractional replication, response surface design, and incompletely randomized blocking design.
▶ STATISTICAL SURVEY METHODOLOGY (STA605) 통계조사방법론
This course covers survey design, survey methodologies (telephone, mail, interview, and Internet surveys), the evaluation of survey questions, and survey research cases.
▶ STATISTICAL METHODS FOR EXPLORATORY DATA ANALYSIS (STA607)
탐색적자료분석방법론
This course examines quantification and visualization methods of multivariate data. Topics include linear dimension reduction methods, such as principal component and correspondence analysis, individual grouping methods, such as K-means clustering and self-organizing maps, the PLS method, and multivariate function visualization.
▶ TIME SERIES ANALYSIS (STA609) 시계열분석
This course covers ARIMAX, including the vector ARIMA model, multivariate GARCH model, unit root test, and cointegration test.
▶ STATISTICAL CONSULTING I (STA610) 통계상담 1
This course covers the skills required for statistical consulting, including data analysis, problem solving, report writing, and oral communication, by conducting consulting sessions with clients.
▶ STATISTICAL CONSULTING II (STA611) 통계상담 2
This course covers the skills required for statistical consulting, including data analysis, problem solving, report writing, and oral communication, by conducting consulting sessions with clients.
▶ SAMPLING THEORY (STA614) 표본추출론
This course introduces problems in sampling methods, decisions about sample size, and sample surveys, as well as the construction of theories for the attainment of goals in given situations and problems in practical application.
▶ QUANTITATIVE FINANCE (STA615) 계량금융
This course covers probability theory, stochastic calculus, the Ito integral, equivalence probability measures for the pricing of derivatives, as well as the fundamentals of pricing theory for options, forward transactions, and futures.
▶ LOSS MODELS (STA616) 손실모형
This course covers the theoretical background of analysis of actuarial data, determining suitable models, and how to provide measures of confidence for decisions based on the model.
▶ SURVIVAL ANALYSIS (STA703) 생존분석
This course covers statistical methods about the parametric method, non-parametric method, and Cox regression method to analyze the data of survival time.
▶ LONGITUDINAL DATA ANALYSIS (STA712) 경시적 자료분석
This course introduces the principles and methods for the analysis of longitudinal data. Topics include graphical data exploration, linear mixed effects models, and generalized linear models for correlated data.
▶ STATISTICAL METHODS IN FUNCTIONAL ESTIMATION (STA713) 함수추정방법
This course introduces estimation methods for statistical models which are defined by functions. The main focus is placed on statistical learning theory, which is a computer intensive statistical method. Spline methodology and several methods for regression and classification function estimation are also explained.
▶ MISSING DATA ANALYSIS (STA714) 결측자료분석
This course covers statistical analysis techniques for incomplete data. Topics include missing data mechanisms, methodologies for analyzing missing data, and multiple imputation.
▶ BIOSTATISTICAL METHODOLOGY (STA715) 생물통계방법론
This course covers statistical concepts and tools that are largely applied to life sciences such as bioinformatics, insurance, or medical/pharmaceutical research including clinical trials.
▶ LATENT VARIABLE MODELS (STA717) 잠재변수
This course covers statistical models using the latent variables, and real data analysis. Topics include the categorical latent models such as latent class analysis (LCA) and latent transition analysis (LTA), and we are interested in in-depth study about these models and real data analysis.
▶ SIMULATION METHODS (STA719) 모의실험방법론
Various simulation methodologies are discussed in this course. Students will practice actual calculation processes through statistical calculation package R. Topics include random number generation, bootstrapping, EM algorithms, and Monte Carlo methods.
▶ LINEAR STRUCTURAL EQUATION MODELS (STA791) 선형구조방정식모형
This course introduces basic concepts of linear structural equation models to analyze the causal relationships between variables and focuses on the methodological aspects and interpretation of analytical results. Students will use the LISREL program in real data analysis.
▶ THEORETICAL STATISTICS (STA801) 이론통계학
This course covers Cramer-Rao inequality, the Neyman, Rao-Blackwell, Neyma-Pearson and Lehmann-Scheffe theorems, MRE and minimax estimation, UMP and UMPU testing, hypothesis testing in linear models, the invariance and minimax principals, and others.
▶ MULTIVARIATE STATISTICAL THEORY (STA803) 다변량통계이론
This course covers classical multivariate normal distribution theory, the union intersection principal, multivariate linear model theory, discriminant and classification analysis, models and reasoning about correlations, and more.
▶ DISCRETE MULTIVARIATE ANALYSIS (STA804) 이산다변량분석
This course covers advanced techniques for discrete multivariate data analysis.
▶ BAYESIAN INFERENCE (STA805) 베이즈추론
This course covers Bayesian estimation in numerical analysis, the principles of Bayesian analysis, and the Bayesian approach in regression analysis in econometrics.
▶ TOPICS IN THEORETICAL STATISTICS I (STA813) 이론통계학특수연구 1
This course introduces major topics in theoretical statistics.
▶ TOPICS IN THEORETICAL STATISTICS II (STA814) 이론통계학특수연구 2
This course introduces major topics in theoretical statistics.
▶ TOPICS IN THEORETICAL STATISTICS III (STA827) 이론통계학특수연구 3
This course introduces major topics in theoretical statistics.
▶ TOPICS IN THEORETICAL STATISTICS IV (STA834) 이론통계학특수연구 4
This course introduces major topics in theoretical statistics.
▶ TOPICS IN APPLIED STATISTICS I (STA829) 응용통계학특수연구 1
This course introduces major topics in applied statistics.
▶ TOPICS IN APPLIED STATISTICS II (STA830) 응용통계학특수연구 2
This course introduces major topics in applied statistics.
▶ TOPICS IN APPLIED STATISTICS III (STA831) 응용통계학특수연구 3
This course introduces major topics in applied statistics.
▶ TOPICS IN APPLIED STATISTICS IV (STA832) 응용통계학특수연구 4
This course introduces major topics in applied statistics.