• International Journal of Reliability and Applications
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• v.7 no.1
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• pp.27-39
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• 2006

Many real world cases in material failure analysis do not follow perfectly the normal distribution. Forcing of the normality assumption may lead to inaccurate predictions and poor product quality. We examine the failure process of the internal bond (IB or tensile strength) of medium density fiberboard (MDF). We propose a forced censoring technique that closer fits the lower tails of strength distributions and better estimates extremely smaller percentiles, which may be valuable to continuous quality improvement initiatives. Further analyses are performed to build an accelerated common-shaped Weibull model for different product types using the $JMP^{(R)}$ Survival and Reliability platform. In this paper, a forced censoring technique is implemented for the first time as a software module, using $JMP^{(R)}$ Scripting Language (JSL) to expedite data processing, which is crucial for real-time manufacturing settings. Also, we use JSL to automate the task of fitting an accelerated Weibull model and testing model homogeneity in the shape parameter. Finally, a package script is written to readily provide field engineers customized reporting for model visualization, parameter estimation, and percentile forecasting. Our approach may be more accurate for product conformance evaluation, plus help reduce the cost of destructive testing and data management due to reduced frequency of testing. It may also be valuable for preventing field failure and improved product safety even when destructive testing is not reduced by yielding higher precision intervals at the same confidence level.

• The Journal of Pediatrics of Korean Medicine
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• v.30 no.4
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• pp.87-98
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• 2016

Objectives The purpose of this study is to evaluate the effect of herbal medicine in children's growth. Methods 51 children from the age of 5 to 16 were participated in this study (27 of boys and 24 of girls). The participants were from the department of the pediatrics in Daegu hanny university oriental medical hospital. They were measured their body composition and their bone age, the height percentile of their first and the last visit. Then, those were compared by the Korean Association of Pediatrics' Growth Statistics Curve. Results 1. Generally, total children's average height and weight were significantly increased after the herbal medical treatment. The differences between their height and the general populations' average height, their weight and general populations' average weight were significantly decreased after the treatment. 2. Total children's average soft lean mass, body fat mass, BMI were also significantly increased after the herbal medical treatment. 3. The mean height percentiles of the children has increased by 1.47 percentile. The mean weight percentiles of the children decreased 1.08 percentile. 4. The height percentiles were increased in every group except the group of boys younger than 9 and older than 12 year old. Other than the group of boys younger than 9-year-old and the group of 10-11-year-old boys, every group showed decreasing weight percentile. Conclusions The herbal medical treatment helped children with growth retardation.

• The KIPS Transactions:PartD
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• v.10D no.6
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• pp.1025-1032
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• 2003

Percentiles are convenient measures of the entire range of values of simulation outputs. However, unlike means and standard deviations, the observations have to be stored since calculation of percentiles requires several passes through the data. Thus, percentile (PE) requires a large amount of computer storage and computation time. The best possible computation time to sort n observations is (O($nlog_{2}n$)), and memory proportional to n is required to store sorted values in order to find a given order statistic. Several approaches for extimating percentiles in RS(regenerative simulation) and non-RS, which can avoid difficulties of PE, have been proposed in [11, 12, 21]. In this paper, we implemented these three approaches known as : leanear PE, batching PE, spectral $P^2$ PE in the context of sequential steady-state simulation. Numerical results of coverage analysis of these PE approachs are present.

• Journal of Forest and Environmental Science
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• v.32 no.3
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• pp.280-290
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• 2016

Stem diameter variability is an essential inventory result that provides useful information in forest management decisions. Little has been done to explore the modelling potentials of standard deviation (SDD) and coefficient of variation (CVD) of diameter at breast height (dbh). This study, therefore, was aimed at developing and testing models for predicting SDD and CVD in stands of Pinus caribaea Morelet (pine) in south west Nigeria. Sixty temporary sample plots of size $20m{\times}20m$, ranging between 15 and 37 years were sampled, covering the entire range of pine in south west Nigeria. The dbh (cm), total and merchantable heights (m), number of stems and age of trees were measured within each plot. Basal area ($m^2$), site index (m), relative spacing and percentile positions of dbh at $24^{th}$, $63^{rd}$, $76^{th}$ and $93^{rd}$ (i.e. $P_{24}$, $P_{63}$, $P_{76}$ and $P_{93}$) were computed from measured variables for each plot. Linear mixed model (LMM) was used to test the effects of locations (fixed) and plots (random). Six candidate models (3 for SDD and 3 for CVD), using three categories of explanatory variables (i.e. (i) only stand size measures, (ii) distribution measures, and (iii) combination of i and ii). The best model was chosen based on smaller relative standard error (RSE), prediction residual sum of squares (PRESS), corrected Akaike Information Criterion ($AIC_c$) and larger coefficient of determination ($R^2$). The results of the LMM indicated that location and plot effects were not significant. The CVD and SDD models having only measures of percentiles (i.e. $P_{24}$ and $P_{93}$) as predictors produced better predictions than others. However, CVD model produced the overall best predictions, because of the lower RSE and stability in measuring variability across different stand developments. The results demonstrate the potentials of CVD in modelling stem diameter variability in relationship with percentiles variables.

• Nutrition Research and Practice
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• v.9 no.4
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• pp.404-410
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• 2015

BACKGROUND/OBJECTIVES: Although the association of body mass index (BMI) with metabolic syndrome (MetS) is well documented, there is little knowledge on the independent and joint associations of BMI and physical activity with MetS risk based on a continuous scoring system. This study was designed to explore the effect of physical activity on interactions between excess body weight and continuous metabolic syndrome (cMetS) in a nationwide survey of Iranian children and adolescents. SUBJECTS/METHODS: Data on 5,625 school students between 10 and 18 years of age were analyzed. BMI percentiles, screen time activity (STA), leisure time physical activity (LTPA) levels, and components of cMetS risk score were extracted. Standardized residuals (z-scores) were calculated for MetS components. Linear regression models were used to study the interactions between different combinations of cMetS, LTPA, and BMI percentiles. RESULTS: Overall, 984 (17.5%) subjects were underweight, whereas 501 (8.9%) and 451 (8%) participants were overweight and obese, respectively. All standardized values for cMetS components, except fasting blood glucose level, were directly correlated with BMI percentiles in all models (P-trend < 0.001); these associations were independent of STA and LTPA levels. Linear associations were also observed among LTPA and standardized residuals for blood pressure, high-density lipoprotein, and waist circumference (P-trend < 0.01). CONCLUSIONS: Our findings suggest that BMI percentiles are associated with cMetS risk score independent of LTPA and STA levels.

• Korean Journal of Health Education and Promotion
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• v.27 no.1
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• pp.105-113
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• 2010

Objcetive: The purpose of this study was to establish body mass index percentiles and cutoffs for overweight and obesity in East Asian Adolescent. Methods: Based on one's age and gender, subjects were selected and measured their weight and height in order to calculate BMI. For BMI cut off points, data were analyzed and percentile curves were established by the modified LMS method. Results were followed by comparing BMI cut off points and percentiles with one's nationality, age and gender. Results: The prevalence of male's overweight and obesity among East Asian adolescent were as follows: Taiwan (15.4%, 9.0%), Korean (14.5%, 8.5%), Chinese (13.3%, 6.6%), and Japanese (6.0%, 2.1%). And for female, Korean (13.1%, 3.7%), Taiwan (12.6%, 5.1%), Chinese (8.3%, 2.3%) and Japanese (7.9%, 3.1%) were in the order. Conclusion: Corresponded to the whole subjects, 11.0% of men were overweight, 4.7 were obesity and that appeared in women with 9.0% in overweight, 3.5% were obese.

• International Journal of Advanced Culture Technology
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• v.12 no.2
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• pp.167-179
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• 2024

This study addresses the challenge of objectively evaluating the performance of early-stage startups amidst limited information and uncertainty. Focusing on companies selected by TIPS, a leading private sector-driven startup support policy in Korea, the research develops a new indicator to assess technological efficiency. By analyzing various input and output variables collected from Crunchbase and KIND (Korea Investor's Network for Disclosure System) databases, including technology use metrics, patents, and Crunchbase rankings, the study derives technological efficiency for TIPS-selected startups. A prediction model is then developed utilizing machine learning techniques such as Random Forest and boosting (XGBoost) to classify startups into efficiency percentiles (10th, 30th, and 50th). The results indicate that prediction accuracy improves with higher percentiles based on the technical efficiency index, providing valuable insights for evaluating and predicting startup performance in early markets characterized by information scarcity and uncertainty. Future research directions should focus on assessing growth potential and sustainability using the developed classification and prediction models, aiding investors in making data-driven investment decisions and contributing to the development of the early startup ecosystem.

• Journal of Preventive Medicine and Public Health
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• v.32 no.1
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• pp.80-87
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• 1999

Objectives. The Study of Small Area Variation(SAV) is most interesting issue in the health care researches. Most studies of SAV have been concluded the existences of variation on the basis of the magnitude of variation without statistical testing. But it is difficult to explain the existence of variation with this way because variation indicies are easily influenced by several parameters and also their distribution are skewed. So, it needs for the study to investigate the distribution of these indices and develop the statistical testing model. Methods. This study was planned to analyze on the distribution of variation indices such as Extremal Quotient(EQ), Coefficient of Variation(CV), Systematic Component of Variation(SCV) and compare the statistical power among indicies. The simulations was performed on the basis of several assumptions and compared to the empirical data. Results. Main findings can be summarized as follows. 1. If other conditions are constant, the more number of regions, the larger 95 percentile of EQ. But under same situation, 95 percentile of CV and SCV were slightly decreased. 2. If the size of regional population or utilization rate were increased, 95 percentile of all statistics were decreased. Also in the cases of small population size and low utilization rate, 95 percentiles of EQ showed various change contrast to the little change of CV. 3. If the difference at the size of regional population were increased, 95 percentiles of EQ and SCV were increased contrast to the little different of CV. 4. If the utilization rate were increased, 95 percentiles of all indicies were increased. But under the same difference of utilization rate, the power of CV and SCV were increased comparing to no change of the power of EQ. 5. Usually the power of EQ were lower than that of CV or SCV and it is similar between CV and SCV. Conclusions. Therefore, we suggest that in selecting the variation indicies at the SAV, CV or SCV are superior than EQ in terms of significance level and power.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.sup3
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• pp.113-117
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• 2016

Quantile regression is an efficient method for predicting and estimating the relationship between explanatory variables and percentile points of the response distribution, particularly for extreme percentiles of the distribution. To study the relationship between urbanization and cancer morbidity, we here applied quantile regression. This cross-sectional study was conducted for 9 cancers in 345 cities in 2007 in Iran. Data were obtained from the Ministry of Health and Medical Education and the relationship between urbanization and cancer morbidity was investigated using quantile regression and least square regression. Fitting models were compared using AIC criteria. R (3.0.1) software and the Quantreg package were used for statistical analysis. With the quantile regression model all percentiles for breast, colorectal, prostate, lung and pancreas cancers demonstrated increasing incidence rate with urbanization. The maximum increase for breast cancer was in the 90th percentile (${\beta}$=0.13, p-value<0.001), for colorectal cancer was in the 75th percentile (${\beta}$=0.048, p-value<0.001), for prostate cancer the 95th percentile (${\beta}$=0.55, p-value<0.001), for lung cancer was in 95th percentile (${\beta}$=0.52, p-value=0.006), for pancreas cancer was in 10th percentile (${\beta}$=0.011, p-value<0.001). For gastric, esophageal and skin cancers, with increasing urbanization, the incidence rate was decreased. The maximum decrease for gastric cancer was in the 90th percentile(${\beta}$=0.003, p-value<0.001), for esophageal cancer the 95th (${\beta}$=0.04, p-value=0.4) and for skin cancer also the 95th (${\beta}$=0.145, p-value=0.071). The AIC showed that for upper percentiles, the fitting of quantile regression was better than least square regression. According to the results of this study, the significant impact of urbanization on cancer morbidity requirs more effort and planning by policymakers and administrators in order to reduce risk factors such as pollution in urban areas and ensure proper nutrition recommendations are made.

• Korean Journal of Community Nutrition
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• v.24 no.6
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• pp.465-475
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• 2019

Objectives: This study investigated dietary and lifestyle factors associated with the weight status among Korean adolescents in multicultural families. Methods: This cross-sectional study analyzed 1,751 multicultural families' adolescents who participated in the 2017-2018 Korea Youth Risk Behavior Surveys. Information on dietary and lifestyle factors was self-reported using a web-based questionnaire and this information included breakfast and foods consumption, perceived health status, alcohol drinking, smoking, physical activity, and weight control efforts. Body mass index (BMI) was calculated based on the self-reported height and body weight (kg/㎡). Weight status was assessed according to the 2017 Korean National Growth Chart: underweight (weight-for-age <5^th percentiles), overweight (85^th≤ BMI-for-age <95^th percentiles), and obese (BMI-for-age ≥95^th percentiles). Multiple logistic regression analysis was performed to examine the dietary and lifestyle factors associated with weight status after adjustment for covariates. Results: Among Korean adolescents from multicultural families, the prevalence of overweight/obesity was 20.9%, whereas about 7% of adolescents were underweight. The weight status did not show differences according to gender, school level, area of residence, and household income. Compared to adolescents who did not have breakfast during the previous week, those who had breakfast 3-4 days/week and ≥5 days/week had a 42% (p=0.021) and a 37% (p=0.009) lower prevalence of overweight/obesity, respectively. The adolescents who frequently consumed carbonated soft drinks (≥5 times/week) showed an odds ratio (OR) of 1.69 (95% CI=1.01-2.83) for overweight/obesity relative to those adolescents who did not consume carbonated soft drinks. The OR of being underweight for adolescents who ate fast food ≥3 times/week was 1.97 (95% CI=1.04-3.71) compared to those adolescents who had not eaten fast food during the previous week. Conclusions: Dietary and lifestyle factors were associated with overweight/obesity as well as underweight among Korean adolescents in multicultural families. Our findings could be used to design and provide nutrition interventions for this specific population.