• Communications for Statistical Applications and Methods
• /
• v.30 no.2
• /
• pp.191-213
• /
• 2023

Unit distributions are frequently used in probability theory and statistics to depict meaningful variables having values between zero and one. Using convenient transformation, the unit inverse exponentiated weibull (UIEW) distribution, which is equally useful for modelling data on the unit interval, is proposed in this study. Quantile function, moments, incomplete moments, uncertainty measures, stochastic ordering, and stress-strength reliability are among the statistical properties provided for this distribution. To estimate the parameters associated to the recommended distribution, well-known estimation techniques including maximum likelihood, maximum product of spacings, least squares, weighted least squares, Cramer von Mises, Anderson-Darling, and Bayesian are utilised. Using simulated data, we compare how well the various estimators perform. According to the simulated outputs, the maximum product of spacing estimates has lower values of accuracy measures than alternative estimates in majority of situations. For two real datasets, the proposed model outperforms the beta, Kumaraswamy, unit Gompartz, unit Lomax and complementary unit weibull distributions based on various comparative indicators.

• Journal of the Korean Society for Marine Environment & Energy
• /
• v.18 no.3
• /
• pp.233-244
• /
• 2015

Marine ecosystem modelling has become a more widely used decision-making tool in coastal ecosystem-based management. However, it is not trivial to develop a well calibrated/validated model with potential applicability and practicality because understanding ecological processes with complexities is a pre-requisite for developing robust ecosystem models and this accompanies a great deal of well coordinated efforts among field-going ecologists, laboratory scientists, modelers, stake-holders and managers. This report aims to provide a brief introduction on two different approaches in marine ecological models: deterministic (mechanistic) and stochastic (statistical) approach. We also included definitions, historical overview of past researches, case studies, and contextual suggestions for coastal management in Korea. A long list of references this report included in this study might be used as an introductory material for those who wish to enter ecosystem modelling field.

• Journal of the Korean School Mathematics Society
• /
• v.14 no.4
• /
• pp.423-442
• /
• 2011

The purpose of this study was to examine the effects of tasks setting for mathematical modelling in the complex real situations. The tasks setting(MMa, MeA) in mathematical modelling was so important that we can't ignore its effects to develop meaning and integrate mathematical ideas. The experimental setting were two groups ($N_1=103$, $N_2=103$) at public high school and non-experimental setting was one group($N_3=103$). In mathematical achievement, we found meaningful improvement for MeA group on modelling tasks, but no meaningful effect on information processing tasks. The statistical method used was ACONOVA analysis. Beside their achievement, we were much concerned about their modelling approach that TSG21 had suggested in Category "Educational & cognitive Midelling". Subjects who involved in experimental works showed very interesting approach as Exploration, analysis in some situation ${\Rightarrow}$ Math. questions ${\Rightarrow}$ Setting models ${\Rightarrow}$ Problem solution ${\Rightarrow}$ Extension, generalization, but MeA group spent a lot of time on step: Exploration, analysis and MMa group on step, Setting models. Both groups integrated actively many heuristics that schoenfeld defined. Specially, Drawing and Modified Simple Strategy were the most powerful on approach step 1,2,3. It was very encouraging that those experimental setting was improved positively more than the non-experimental setting on mathematical belief and interest. In our school system, teaching math. modelling could be a answer about what kind of educational action or environment we should provide for them. That is, mathematical learning.

• Journal of Korean Academy of Nursing
• /
• v.30 no.1
• /
• pp.148-159
• /
• 2000

The purpose of this study was to test the theoretical model designed to explain juvenile delinquency by media violence. Data were collected through questionnaire survey over a period of 3 months. Subjects served for this study consisted of 537 adolescents including 217 delinquent adolescents and 320 student adolescents in Korea, sampled from Korean student population and delinquent adolescent population confined in juvenile correctional institutions, using proportional stratified random sampling method. In this study, exogeneous variable was family dynamic environment and endogeneous variables were character of adolescent including need satisfaction/ frustration, sociability, antisocial personality tendency, complaints of psychosomatic symptoms and depressive trend, juvenile delinquent behavior and media violence themes including the extent of interest in and exposure and modelling impulsiveness and modelling to media violence themes. A total of 18 instruments were used to operationalized concepts in this model. A validation study indicated that internal consistencies for the 18 instruments which the researcher used were reliable. The one month test-retest correlation for these instruments ranged from 0.54 to 0.88. Statistical methods employed were descriptive statistics and covariance structural modelling. In summarized conclusion, it was found that media violence served as the most contributor to juvenile delinquency by direct effect of 0.64(t=10.18). That is, as the adolescents have to be the higher extent of interest in and exposure and modelling impulsiveness and modelling to media violence themes, they will show the more frequency of delinquent behavior. The single most powerful contributor by total effect of 0.73(t=7.90) (direct effect=0.19, indirect effect=0.54) to the development of delinquent behavior identified in this study was a construct defining family dynamic environment. That is, as the adolescents had to be more unstable family dynamic environment, they became more frustrated to their psychological need, and revealed the more maladaptive personality pattern, consequently they behaved the higher misconducts such as juvenile delinquency through media violence.

• The Korean Journal of Applied Statistics
• /
• v.13 no.2
• /
• pp.407-414
• /
• 2000

Carroll and Ruppert(1988) analyzed the esterase assay data with regression model based on quasi-likelihood. Jung and Lee(1997) introduced a goodness-of-fit test for testing the adequacy of the quasi-likelihood and claimed that there is no gross inadequacy with the model because their test was not rejected. However, Lee and Xelder(199S)'s residual plots revealed that the model did not sufficiently reflect the increase of the variance with that of the mean. In this paper, we re-analyze the esterase assay data with the joint modelling of mean and dispersion in Lee and l\elder(1998) and evaluate the validity of the fitted model by applying the residual plots. And it is illustrated that Lee and Nelder(199S)'s restricted likelihood is more efficient in goodness-of-fit test for the dispersion model.

• Computers and Concrete
• /
• v.24 no.1
• /
• pp.7-17
• /
• 2019

The complex phenomenon of the bond formation in geopolymer is not well understood and therefore, difficult to model. This paper present applied statistical models for evaluating the compressive strength of geopolymer. The applied statistical models studied are divided into three different categories - linear regression [least absolute shrinkage and selection operator (LASSO) and elastic net], tree regression [decision and bagging tree] and kernel methods (support vector regression (SVR), kernel ridge regression (KRR), Gaussian process regression (GPR), relevance vector machine (RVM)]. The performance of the methods is compared in terms of error indices, computational effort, convergence and residuals. Based on the present study, kernel based methods (GPR and KRR) are recommended for evaluating compressive strength of Geopolymer concrete.

• The Korean Journal of Applied Statistics
• /
• v.27 no.3
• /
• pp.419-430
• /
• 2014

An accurate prediction of electricity supply and demand is important for daily life, industrial activities, and national management. In this paper electricity sales is predicted by time series modelling. Real data analysis shows the transfer function model with cooling and heating days as an input time series and a pulse function as an intervention variable outperforms other time series models for the root mean square error and the mean absolute percentage error.

• The Korean Journal of Applied Statistics
• /
• v.6 no.2
• /
• pp.253-263
• /
• 1993

Recent interest in Taguchi's methods have led to developments of joint modelling of the mean and dispersion in generalized linear models. Since a single data transformation cannot produce all the necessary conditions for an analysis, for the analysis of the Taguchi data, the use of the generalized linear models is preferred to a commonly used data transformation method. In this paper, we will illustrate this point and provide GLIM macros to implement the joint modelling of the mean and dispersion in generalized linear models.

• Wind and Structures
• /
• v.28 no.6
• /
• pp.369-380
• /
• 2019

Statistical distributions are very useful in describing wind speed characteristics and in predicting wind power potential of a specified region. Although the Weibull distribution is the most popular one in wind energy literature, it does not seem to be able to perfectly fit all the investigated wind speed data in nature. Thus, many studies are still being conducted to find flexible distribution for modelling wind speed data. In this study, we propose a new Odd-Burr Rayleigh distribution for wind speed characterization. The Odd-Burr Rayleigh distribution with two shape parameters is flexible enough to model different shapes of wind speed data and thus it can be an alternative wind speed distribution for the assessment of wind energy potential. Therefore, suitability of the Odd-Burr Rayleigh distribution is investigated on real wind speed data taken from different regions in the South Africa. Numerical results of the conducted analysis confirm that the new Odd-Burr Rayleigh distribution is suitable for modelling most of the considered real wind speed cases and it also can be used for predicting wind power.

• International Journal of Computer Science & Network Security
• /
• v.22 no.2
• /
• pp.185-192
• /
• 2022

Forecasting and time series modelling plays a vital role in the data analysis process. Time Series is widely used in analytics & data science. Forecasting stock prices is a popular and important topic in financial and academic studies. A stock market is an unregulated place for forecasting due to the absence of essential rules for estimating or predicting a stock price in the stock market. Therefore, predicting stock prices is a time-series problem and challenging. Machine learning has many methods and applications instrumental in implementing stock price forecasting, such as technical analysis, fundamental analysis, time series analysis, statistical analysis. This paper will discuss implementing the stock price, forecasting, and research using prophet and LSTM models. This process and task are very complex and involve uncertainty. Although the stock price never is predicted due to its ambiguous field, this paper aims to apply the concept of forecasting and data analysis to predict stocks.