• The Korean Journal of Applied Statistics
• /
• v.27 no.6
• /
• pp.909-922
• /
• 2014

This paper reviews Bayesian inference with inequality constraints. It focuses on ⅰ) comparison of models with various inequality/equality constraints on parameters, ⅱ) multiple tests on equalities of parameters when parameters are under inequality constraints, ⅲ) multiple test on equalities of score parameters in models for contingency tables with ordinal categorical variables.

• Education of Primary School Mathematics
• /
• v.22 no.4
• /
• pp.205-221
• /
• 2019

The study aimed at investigating preservice elementary school teachers' statistical reasoning when they posed survey questions as they engaged in statistical problem solving, and analyzing how their statistical reasoning affect the subsequent stages. 24 groups of sophomore students(80 students) from two education universities conducted statistical problem solving and completed statistical report, and 22 of them were analyzed. As a result, 9 statistical reasoning were shown when preservice teachers posed survey questions. Among them, question clarification oriented reasoning and variability based reasoning were not exclusively focused upon in the previous research. In order to investigate how statistical reasoning in posing survey questions affected subsequent stages, we examined difficulties and issues that preservice teachers had when they engaged in analyses and conclusion stage described in their report. Consequently, preservice teachers' difficulties were related to population relevant reasoning, category level reasoning, standardization reasoning, alignment to question reasoning, and question clarification oriented reasoning. While previous studies did not focus on question posing stage, this study claimed the necessity of emphasizing various statistical reasoning in question posing and importance of teaching and learning method of appropriate statistical reasoning in question posing.

• The Korean Journal of Applied Statistics
• /
• v.31 no.1
• /
• pp.155-165
• /
• 2018

Outliers and influential observations often distort many numerical measures for data analysis. Jang and Anderson-Cook (Quality and Reliability Engineering International, 30, 1409-1425, 2014) proposed a graphical firework plot method for exploratory analysis purpose to provide a possible visualization of the trace of the impact of the possible outlying and influential observations on the univariate/bivariate data analysis and regression. They developed 3-D plot as well as pairwise plot for the appropriate measures of interest. We use firework plots as a graphical exploratory data analysis tool to detect outliers and evaluate the impact of outliers in statistical inference.

• The Korean Journal of Applied Statistics
• /
• v.28 no.2
• /
• pp.123-136
• /
• 2015

Science has developed with great achievements after Galileo's discovery of the law depicting a relationship between observable variables. However, many natural phenomena have been better explained by models including unobservable random effects. A mixed effect model was the first statistical model that included unobservable random effects. The importance of the mixed effect models is growing along with the advancement of computational technologies to infer complicated phenomena; subsequently mixed effect models have extended to various statistical models such as hierarchical generalized linear models. Hierarchical likelihood has been suggested to estimate unobservable random effects. Our special issue about mixed effect models shows how they can be used in statistical problems as well as discusses important needs for future developments. Frequentist and Bayesian approaches are also investigated.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2021.06a
• /
• pp.165-165
• /
• 2021

전 세계적으로 하천 수질이 저하되고 있다. 이는 수생태계의 손상을 야기하고 인류를 위한 하천의 물 공급원 기능에 저해 요소로 작용한다. 오염된 수생태계에 대한 효과적인 맞춤형 관리전략을 수립하려면 수질의 시공간적 변동 요인을 이해하는 것이 중요하다. 시간적 및 공간적 변동성이 모두 중요하지만, 본 연구에서는 낙동강 전체 유역에서 나타나는 공간적인 변동성에 집중하여 하천 수질의 공간적 차이에 영향을 미치는 요인을 식별하고 그들의 상대적 중요성을 분석하고자 하였다. 분석을 위해 낙동강 유역 전역의 40개 수질오염총량관리 단위유역에서 5년 동안 수집된 하천 총인 농도와 유역특성 자료를 사용하였다. 총인 농도의 공간적 변동성에 영향을 미치는 주요 유역특성을 식별하기 위한 통계모델 선정을 위해 완전 탐색 접근법과 베이지안 추론이 적용되었다. 완전 탐색은 두 단계에 걸쳐 진행되었으며, 1 단계 완전 탐색의 결과로 유역특성 자료들의 중요도가 선정되었으며 2 단계 완전탐색 결과로 통계모델이 우선 선정되었다. 우선 선정된 통계모델은 베이지안 추론을 통해 모델의 정확도와 불확실성이 분석되었고 공간적 변동성 분석을 위한 최적 모델이 선정되었다. 본 연구의 결과로 낙동강 하천 총인 농도의 공간적 변동성에 영향을 미치는 주요 유역특성에 대한 통찰력이 제공된다. 또한 식별된 주요 유역특성은 유역특성 변화에 대한 하천의 수질 반응을 예측하는데 사용될 수 있을 것으로 기대된다.

• The Korean Journal of Applied Statistics
• /
• v.29 no.6
• /
• pp.1107-1116
• /
• 2016

Recent astronomical survey observations have produced substantial amounts of data as well as completely changed conventional methods of analyzing astronomical data. Both classical statistical inference and modern machine learning methods have been used in every step of data analysis that range from data calibration to inferences of physical models. We are seeing the growing popularity of using machine learning methods in classical problems of astronomical data analysis due to low-cost data acquisition using cheap large-scale detectors and fast computer networks that enable us to share large volumes of data. It is common to consider the effects of inhomogeneous spatial and temporal coverage in the analysis of big astronomical data. The growing size of the data requires us to use parallel distributed computing environments as well as machine learning algorithms. Distributed data analysis systems have not been adopted widely for the general analysis of massive astronomical data. Gathering adequate training data is expensive in observation and learning data are generally collected from multiple data sources in astronomy; therefore, semi-supervised and ensemble machine learning methods will become important for the analysis of big astronomical data.

• The Korean Journal of Applied Statistics
• /
• v.19 no.1
• /
• pp.97-110
• /
• 2006

Since it is well-established that even high quality data tend to contain outliers, one would expect fat? greater reliance on robust regression techniques than is actually observed. But most of all robust regression estimators suffers from the computational difficulties and the lower efficiency than the least squares under the normal error model. The weighted self-tuning estimator (WSTE) recently suggested by Lee (2004) has no more computational difficulty and it has the asymptotic normality and the high break-down point simultaneously. Although it has better properties than the other robust estimators, WSTE does not have full efficiency under the normal error model through the weighted least squares which is widely used. This paper introduces a new approach as called the reweighted WSTE (RWSTE), whose scale estimator is adaptively estimated by the self-tuning constant. A Monte Carlo study shows that new approach has better behavior than the general weighted least squares method under the normal model and the large data.

• The Korean Journal of Applied Statistics
• /
• v.24 no.5
• /
• pp.951-961
• /
• 2011

In this paper, we propose a Bayesian inference using the Markov Chain Monte Carlo(MCMC) method for the zero inflated negative binomial(ZINB) regression model. The proposed model allows the regression model for zero inflation probability as well as the regression model for the mean of the dependent variable. This extends the work of Jang et al. (2010) to the fully defiend ZINB regression model. In addition, we apply the proposed method to a real data example, and compare the efficiency with the zero inflated Poisson model using the DIC. Since the DIC of the ZINB is smaller than that of the ZIP, the ZINB model shows superior performance over the ZIP model in zero inflated count data with overdispersion.

• The Korean Journal of Applied Statistics
• /
• v.27 no.7
• /
• pp.1077-1095
• /
• 2014

Unlike randomized trial, statistical strategies for inferring the unbiased causal relationship are required in the observational studies. Recently, new methods for the causal inference in the observational studies have been proposed such as the matching with the propensity score or the inverse probability treatment weighting. They have focused on how to control the confounders and how to evaluate the effect of the treatment on the result variable. However, these conventional methods are valid only when the treatment variable is categorical and both of the treatment and the result variables are directly observable. Research on the causal inference can be challenging in part because it may not be possible to directly observe the treatment and/or the result variable. To address this difficulty, we propose a method for estimating the average causal effect when both of the treatment and the result variables are latent. The latent class analysis has been applied to calculate the propensity score for the latent treatment variable in order to estimate the causal effect on the latent result variable. In this work, we investigate the causal effect of adolescents delinquency on their substance use using data from the 'National Longitudinal Study of Adolescent Health'.

• The Korean Journal of Applied Statistics
• /
• v.27 no.6
• /
• pp.1039-1047
• /
• 2014

An autoregressive model with normal errors is a natural model that attempts to fit time series data. More flexible models that include normal distribution as a special case are necessary because they can cover normality to non-normality models. The skewed exponential power distribution is a possible candidate for autoregressive models errors that may have tails lighter(platykurtic) or heavier(leptokurtic) than normal and skewness; in addition, the use of skewed exponential power distribution can reduce the influence of outliers and consequently increases the robustness of the analysis. We use SIR algorithm and grid method for an efficient Bayesian estimation.