What is he best known for?
The fields of study he is best known for:
- Statistics
- Normal distribution
- Machine learning
His main research concerns Statistics, Acceptable quality limit, Control chart, Sampling and EWMA chart.
His study brings together the fields of Group and Statistics.
His Acceptable quality limit research is multidisciplinary, incorporating elements of Process capability index, Plan, Reliability engineering and Normal distribution.
The various areas that Chi-Hyuck Jun examines in his Plan study include Quality level and Operations research.
His EWMA chart research includes elements of Algorithm and Chart.
His Weibull distribution research focuses on subjects like Shape parameter, which are linked to Consumer's risk and Scale parameter.
His most cited work include:
- Performance of some variable selection methods when multicollinearity is present (1140 citations)
- A simple and fast algorithm for K-medoids clustering (947 citations)
- Repetitive group sampling procedure for variables inspection (133 citations)
What are the main themes of his work throughout his whole career to date?
Chi-Hyuck Jun spends much of his time researching Statistics, Control chart, Plan, EWMA chart and Acceptance sampling.
His study involves Weibull distribution, Sampling, Producer's risk, Statistic and Consumer's risk, a branch of Statistics.
His study explores the link between Control chart and topics such as Chart that cross with problems in Algorithm, Average run length and Process control.
His Plan research integrates issues from Quality, Group, Reliability engineering, Acceptable quality limit and Operations research.
The EWMA chart study combines topics in areas such as Moving average and Exponential distribution.
Chi-Hyuck Jun has included themes like Percentile and Sample size determination in his Acceptance sampling study.
He most often published in these fields:
- Statistics (46.83%)
- Control chart (23.87%)
- Plan (20.24%)
What were the highlights of his more recent work (between 2016-2021)?
- Statistics (46.83%)
- Control chart (23.87%)
- Chart (14.20%)
In recent papers he was focusing on the following fields of study:
Statistics, Control chart, Chart, EWMA chart and Sampling are his primary areas of study.
His Statistics research incorporates elements of Quality and Process capability index.
As part of the same scientific family, Chi-Hyuck Jun usually focuses on Control chart, concentrating on Algorithm and intersecting with Feature selection.
In Chart, Chi-Hyuck Jun works on issues like Binomial distribution, which are connected to Nonparametric statistics.
Chi-Hyuck Jun interconnects Poisson distribution and Moving average in the investigation of issues within EWMA chart.
The various areas that he examines in his Sampling study include State and State.
Between 2016 and 2021, his most popular works were:
- Instance categorization by support vector machines to adjust weights in AdaBoost for imbalanced data classification (50 citations)
- Privacy-Preserving Patient Similarity Learning in a Federated Environment: Development and Analysis. (32 citations)
- A control chart for multivariate Poisson distribution using repetitive sampling (19 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Machine learning
- Normal distribution
The scientist’s investigation covers issues in Statistics, Control chart, EWMA chart, X-bar chart and Chart.
His research brings together the fields of Process capability index and Statistics.
His biological study spans a wide range of topics, including Reliability engineering and Acceptance sampling.
His studies examine the connections between Control chart and genetics, as well as such issues in Poisson distribution, with regards to c-chart.
As part of one scientific family, Chi-Hyuck Jun deals mainly with the area of EWMA chart, narrowing it down to issues related to the Statistic, and often CUSUM and Burr distribution.
His studies deal with areas such as Shewhart individuals control chart and u-chart as well as X-bar chart.
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