• Title/Summary/Keyword: Bootstrap Confidence Interval

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Evaluation of Probability Rainfalls Estimated from Non-Stationary Rainfall Frequency Analysis (비정상성 강우빈도해석법에 의한 확률강우량의 평가)

  • Lee, Chang-Hwan;Ahn, Jae-Hyun;Kim, Tae-Woong
    • Journal of Korea Water Resources Association
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    • v.43 no.2
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    • pp.187-199
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    • 2010
  • This study evaluated applicability and confidence of probability rainfalls estimated by the non-stationary rainfall frequency analysis which was recently developed. Using rainfall data at 4 sites which have an obvious increasing trend in observations, we estimated 3 type probability rainfalls; probability rainfalls from stationary rainfall frequency analysis using data from 1973-1997, probability rainfalls from stationary rainfall frequency analysis using data from 1973-2006, probability rainfalls from non-stationary rainfall frequency analysis assuming that the current year is 1997 and the target year is 2006. Based on the comparison of residuals from 3 probability rainfalls, the non-stationary rainfall frequency analysis provided more effective and well-directed estimates of probability rainfalls in the target year. Using Bootstrap resampling, this study also evaluated the parameter estimation methods for the non-stationary rainfall frequency analysis based on confidence intervals. The confidence interval length estimated by the maximum likelihood estimation (MLE) is narrower than the probability weighted moments (PWM). The results indicated that MLE provides more proper confidence than PWM for non-stationary probability rainfalls.

Relationship Analysis on the Monitoring Period and Parameter Estimation Error of the Coastal Wave Climate Data (연안 파랑 관측기간과 모수추정 오차 관계분석)

  • Cho, Hongyeon;Jeong, Weon-Mu;Jun, Ki Cheon
    • Journal of Korean Society of Coastal and Ocean Engineers
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    • v.25 no.1
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    • pp.34-39
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    • 2013
  • In this study, the quantitative analysis and pattern analysis of the error bounds with respect to recording period were carried out using the wave climate data from coastal areas. Arbitrary recording periods were randomly sampled from one month to six years using the bootstrap method. Based on the analysis, for recording periods less than one year, it was found that the error bounds decreased rapidly as the recording period increased. Meanwhile, the error bounds were found to decrease more slowly for recording periods longer than one year. Assuming the absolute estimate error to be around 10% (${\pm}0.1m$) for an one meter significant wave height condition, the minimum recording period for reaching the estimate error for Sokcho and Geoje-Hongdo stations satisfied this condition with over two years of data, while Anmado station was found to satisfy this condition when using observational data of over three years. The confidence intervals of the significant wave height clearly show an increasing pattern when the percentile value of the wave height increases. Whereas, the confidence intervals of the mean wave period are nearly constant, at around 0.5 seconds except for the tail regions, i.e., 2.5- and 97.5-percentile values. The error bounds for 97.5-percentile values of the wave height necessary for harbor tranquility analysis were found to be 0.75 m, 0.5 m, and 1.2 m in Sokcho, Geoje-Hongdo, and Anmado, respectively.

Relationship of Workplace Violence to Turnover Intention in Hospital Nurses: Resilience as a Mediator (병원 간호사의 직장 폭력경험과 이직의도의 관계에서 극복력의 매개작용)

  • Kang, Hyun-Jung;Shin, Jaeyong;Lee, Eun-Hyun
    • Journal of Korean Academy of Nursing
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    • v.50 no.5
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    • pp.728-736
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    • 2020
  • Purpose: This study aimed to identify the relationship between workplace violence and turnover intention, and the mediation effect of resilience on the relationship in hospital nurses. Methods: This was a cross-sectional study. A total of 237 registered nurses were recruited from three hospitals in South Korea from April to May 2019. Participants were invited to complete self-reported questionnaires that measure workplace violence, turnover intention, resilience, and demographic information. The data obtained were analyzed using multiple regression and a simple mediation model applying the PROCESS macro with 95% bias-corrected bootstrap confidence interval (5,000 bootstrap resampling). Results: After controlling demographic covariates, workplace violence significantly accounted for the variance of turnover intention. It was also demonstrated that resilience partially mediated the relationship between workplace violence and turnover intention in hospital nurses. A 73.8% of nurses had experienced workplace violence (such as attack on personality, attack on professional status, isolation from work, or direct attack). Conclusion: Workplace violence directly influences turnover intention of nurses and indirectly influences it through resilience. Therefore, hospital administrators need to develop and provide a workplace violence preventive program and resilience enhancement program to decrease nurses' turnover intention, and leaving.

Design of the Staircase Fatigue Tests for the Random Fatigue Limit Model (확률적 피로한도모형하에서 계단형 피로시험의 설계)

  • Seo, Sun-Keun;Park, Jung-Eun;Cho, You-Hee;Song, Suh-Il
    • Journal of Korean Society for Quality Management
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    • v.35 no.3
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    • pp.107-117
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    • 2007
  • The fatigue has been considered the most failure mode of metal, ceramic, and composite materials. In this paper, numerical experiments to asses the usefulness of two Dixon's methods(small and large samples) and 14 S-N methods on assumptions of lognormal fatigue limit distribution under RFL(Random Fatigue Limit) model are conducted for staircase(or up-and-down) test and compared by MSE(Mean Squared Error) and bias for estimates of mean log-fatigue limit. Also, guidelines for staircase test plans to choose initial stress level and step size are recommended from numerical experiments including sensitivity analyses. In addition, the parametric bootstrap method to construct a confidence interval for the mean of log-fatigue limit by the percentile method using a transition probability matrix of Markov chain is presented and illustrated with an example.

Optimal Monitoring Frequency Estimation Using Confidence Intervals for the Temporal Model of a Zooplankton Species Number Based on Operational Taxonomic Units at the Tongyoung Marine Science Station

  • Cho, Hong-Yeon;Kim, Sung;Lee, Youn-Ho;Jung, Gila;Kim, Choong-Gon;Jeong, Dageum;Lee, Yucheol;Kang, Mee-Hye;Kim, Hana;Choi, Hae-Young;Oh, Jina;Myong, Jung-Goo;Choi, Hee-Jung
    • Ocean and Polar Research
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    • v.39 no.1
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    • pp.13-21
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    • 2017
  • Temporal changes in the number of zooplankton species are important information for understanding basic characteristics and species diversity in marine ecosystems. The aim of the present study was to estimate the optimal monitoring frequency (OMF) to guarantee and predict the minimum number of species occurrences for studies concerning marine ecosystems. The OMF is estimated using the temporal number of zooplankton species through bi-weekly monitoring of zooplankton species data according to operational taxonomic units in the Tongyoung coastal sea. The optimal model comprises two terms, a constant (optimal mean) and a cosine function with a one-year period. The confidence interval (CI) range of the model with monitoring frequency was estimated using a bootstrap method. The CI range was used as a reference to estimate the optimal monitoring frequency. In general, the minimum monitoring frequency (numbers per year) directly depends on the target (acceptable) estimation error. When the acceptable error (range of the CI) increases, the monitoring frequency decreases because the large acceptable error signals a rough estimation. If the acceptable error (unit: number value) of the number of the zooplankton species is set to 3, the minimum monitoring frequency (times per year) is 24. The residual distribution of the model followed a normal distribution. This model can be applied for the estimation of the minimal monitoring frequency that satisfies the target error bounds, as this model provides an estimation of the error of the zooplankton species numbers with monitoring frequencies.

Efficiency Analysis of Project Management Offices Using Bootstrap DEA (부트스트랩 자료포락분석을 이용한 프로젝트 관리 조직의 효율성 분석)

  • Ko, Joong-Hoon;Park, Sung-Hun;Bae, Eun-Song;Kim, Dae-Cheol
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.41 no.3
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    • pp.61-71
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    • 2018
  • The purpose of this study is to analyze the efficiencies of project management offices in large information system construction projects using the data envelopment analysis. In addition, we tried to estimate the confidence interval of those efficiencies using bootstrap DEA to give a statistical meaning. The efficiency by the CCR model is analyzed as eight project management offices are fully efficient and 22 project management offices are inefficient. On the other hand, there are 15 project management offices are fully efficient, but 15 project management offices are inefficient in the BCC model. As the result of the scale efficiencies, of the inefficient project management offices, 13 project management offices are inefficient in scale. It is possible to eliminate the inefficiency in the CCR model by improving their project performances. And, the nine project management offices showed that the inefficiency was due to pure technical efficiency, and these companies should look for various improvements such as improvement of project execution system and project management process. In order that the inefficient project management offices be efficient, it is analyzed that more efforts must be made for on-budget and on-time as a result of examining the potential improvement potentials of inefficient project management offices.

Self-care in Patients with Atrial Fibrillation Based on the Theory of Unpleasant Symptoms (불쾌 증상 이론을 적용한 심방 세동 환자의 자가관리)

  • Kim, Min Young;Lee, Sun Hee;Park, Han Jong
    • Journal of Korean Clinical Nursing Research
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    • v.28 no.1
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    • pp.23-33
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    • 2022
  • Purpose: This study aimed to identify the relationships among disease severity, anxiety, depression, social support, unpleasant symptoms and self-care among patients with atrial fibrillation based on the unpleasant symptom theory, and to examine the mediating effects of unpleasant symptoms. Methods: A cross-sectional study was conducted. The participants were 216 patients with atrial fibrillation who were being followed up on an outpatient basis at a university hospital in Seoul. Data were collected from November 1, 2020 to June 30, 2021, using self-report questionnaires. Data were analyzed using IBM SPSS/WIN 27.0 and PROCESS macro with 95% bias-corrected bootstrap confidence interval(CI). Results: The average age of participants in this study was 66.0years. Disease severity (β=10.19, p<.001) and depression (β=1.53, p<.001) had significant positive relationships with unpleasant symptoms. Also, unpleasant symptoms (β=-0.03, p=.006) had a negative relationship with physical activity, which is a subscale of self- care. Social support (β=0.06, p<.001) was positively related with physical activity. Unpleasant symptoms showed a mediation effect in the relationship between disease severity and physical activity (Bias corrected bootstrap CI -0.65, -0.04). Depression had an indirect effect on physical activity that was mediated by unpleasant symptoms (Bias corrected bootstrap CI -0.11, -0.00). Conclusion: The findings of this study suggest that integrated strategies including physical, psychological, and social factors should be considered to promote self-care in patients with atrial fibrillation.

Estimating variation in the microbiological quality of seasoned soybean sprouts using probability model (확률 모형을 이용한 콩나물 무침의 미생물적 품질 변화 예측)

  • Park, Jin-Pyo
    • Journal of the Korean Data and Information Science Society
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    • v.21 no.5
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    • pp.909-916
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    • 2010
  • This study aims to establish storage stability conditions for cook-chilled korean ethenic foods. In order to achieve this aims, we establish a probability model of microbial counts of cook-chilled korean side dishes product-seasoned soybean sprouts. And seasoned soybean sprouts were stored during 1 to 5 days under constant temperature conditions at 0, 5, 10 and $15^{\circ}C$. Next we find confidence intervals for variation in the microbiological quality of seasoned soybean sprouts.

Environmental Dependence of Luminosity-Size Relation of Local Galaxies

  • Ann, Hong Bae
    • Journal of the Korean earth science society
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    • v.38 no.5
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    • pp.333-344
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    • 2017
  • We present the environmental dependence of the luminosity-size relation of galaxies in the local universe (z < 0.01) along with their dependence on galaxy morphology represented by five broad types (E, dEs, S0, Sp, and Irr). The environmental parameters we consider are the local background density and the group/cluster membership together with the clustercenteric distance for the Virgo cluster galaxies. We derive the regression coefficient (${\beta}$), i.e., the slope of the line representing the least-squares fitting to the data and the Pearson correlation coefficient (c.c.) representing the goodness of the least-squares fit along with the confidence interval from bootstrap resampling. We find no significant dependence of the luminosity-size relation on galaxy morphology. However, there is a weak dependence of the luminosity-size relations on the environment of galaxies, in the sense that galaxies in the low density environment have shallower slopes than galaxies in the high density regions except for elliptical galaxies that show an opposite trend.

The Analysis of the Number of Donations Based on a Mixture of Poisson Regression Model (포아송 분포의 혼합모형을 이용한 기부 횟수 자료 분석)

  • Kim In-Young;Park Su-Bum;Kim Byung-Soo;Park Tae-Kyu
    • The Korean Journal of Applied Statistics
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    • v.19 no.1
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    • pp.1-12
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    • 2006
  • The aim of this study is to analyse a survey data on the number of charitable donations using a mixture of two Poisson regression models. The survey was conducted in 2002 by Volunteer 21, an nonprofit organization, based on Koreans, who were older than 20. The mixture of two Poisson distributions is used to model the number of donations based on the empirical distribution of the data. The mixture of two Poisson distributions implies the whole population is subdivided into two groups, one with lesser number of donations and the other with larger number of donations. We fit the mixture of Poisson regression models on the number of donations to identify significant covariates. The expectation-maximization algorithm is employed to estimate the parameters. We computed 95% bootstrap confidence interval based on bias-corrected and accelerated method and used then for selecting significant explanatory variables. As a result, the income variable with four categories and the volunteering variable (1: experience of volunteering, 0: otherwise) turned out to be significant with the positive regression coefficients both in the lesser and the larger donation groups. However, the regression coefficients in the lesser donation group were larger than those in larger donation group.