• Communications for Statistical Applications and Methods
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• v.19 no.1
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• pp.177-182
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• 2012

A structural break in the level as well as in the innovation variance has often been exhibited in economic time series. In this paper we propose robust unit root tests based on a sign-type test statistic when a time series has a shift in its level and the corresponding volatility. The proposed tests are robust to a wide class of partially stationary processes with heavy-tailed errors, and have an exact binomial null distribution. Our tests are not affected by the size or location of the break. We set the structural break under the null and the alternative hypotheses to relieve a possible vagueness in interpreting test results in empirical work. The null hypothesis implies a unit root process with level shifts and the alternative connotes a stationary process with level shifts. The Monte Carlo simulation shows that our tests have stable size than the OLSE based tests.

• Communications for Statistical Applications and Methods
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• v.24 no.4
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• pp.367-382
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• 2017

We consider an infinite-order long-memory heterogeneous autoregressive (HAR) model, which is motivated by a long-memory property of realized volatilities (RVs), as an extension of the finite order HAR-RV model. We develop bootstrap tests for structural mean or variance changes in the infinite-order HAR model via stationary bootstrapping. A functional central limit theorem is proved for stationary bootstrap sample, which enables us to develop stationary bootstrap cumulative sum (CUSUM) tests: a bootstrap test for mean break and a bootstrap test for variance break. Consistencies of the bootstrap null distributions of the CUSUM tests are proved. Consistencies of the bootstrap CUSUM tests are also proved under alternative hypotheses of mean or variance changes. A Monte-Carlo simulation shows that stationary bootstrapping improves the sizes of existing tests.

• Fisheries and Aquatic Sciences
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• v.1 no.1
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• pp.48-62
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• 1998

Seasonal volume transport on the Texas-Louisiana continental shelf is investigated in terms of objectively fitted transport streamfunction fields based on the current meter data of the Texas­Louisiana Shelf Circulation and Transport Processes Study. Adopted here for the objective mapping is a method employing a two-dimensional truncated Fourier representation of the streamfunction over a domain, with the amplitudes determined by least square fit of the observation. The fitting was done with depth-averaged flow rather than depth-integrated flow to reduce the root-mean-square error. The fitting process filters out $11\%$ of the kinetic energy in the monthly mean transport fields. The shelf-wide pattern of streamfunction fields is similar to that of near-surface velocity fields over the region. The nearshore transport, about 0.1 to 0.3 Sv $(1 Sv= 10^6\;m^3/sec)$, is well correlated with the seasonal signal of along-shelf wind stress. The spring transport is weak compared to other seasons in the inner shelf region. The transport along the shelf break is large and variable. In the southwestern shelf break, transport amounts up to 4.7 Sv, which is associated with the activities of the encroaching of energetic anticyclonic eddies originated in Loop Current of the eastern Gulf of Mexico. The first empirical orthogonal function (EOF) of streamfunction variability contains $67.3\%$ of the variance and shows a simple, shelf-wide, along-shelf pattern of transport. The amplitude evolution of the first EOF is highly correlated (correlation coefficient: 0.88) with the evolution of the along-shelf wind stress. This provides strong evidence that the large portion of seasonal variation of the shelf transport is wind-forced. The second EOF contains $23.7\%$ of the variance and shows eddy activities at the southwestern shelf break. The correlation coefficient between the amplitudes of the second EOF and wind stress is 0.42. We assume that this mode is coupled a periodic inner shelf process with a non-periodic eddy process on the shelf break. The third EOF (accounting for $7.2\% of the variance) shows several cell structures near the shelf break associated with the variability of the Loop Current Eddies. The amplitude time series of the third EOF show little correlation with the along-shelf wind.

• Korean Journal of Health Education and Promotion
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• v.21 no.3
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• pp.195-211
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• 2004

Stress is a primary health promotion issue in worksite research because psychological distress is closely related not only to workers' health status but also to their job performance. This study identified the significant health behaviors affecting workers' job-related stress in Korean manufacturing industry with the national survey data conducted by the Korean Occupational Safety and Health Agency in 2003. A total of 7,818 factory workers in 1,562 manufacturing companies participated in the Korean nation-wide occupational health survey and 3,390 workers answered that they had any stressors in their workplace among the 7,818 workers finally participated in the analysis. Participants were selected by the stratified proportional sampling process by manufacturing industry classification, company size, and company locations (8 metropolitan and 8 non-metropolitan regions) in Korea. Trained interviewers visited the target companies and interviewed the factory workers randomly selected in each company. Smoking, drinking, weight control, exercise, sleeping, break time at work, and perceived fatigue were included in the health behavior construct. Stress symptoms was consisted of physical and psychological stress with 8 items. All survey responses were anonymously coded into the SPSS statistical program and testified using stepwise multiple regression analysis. Male workers were 73.5% and the 30s were 40.0% among the age groups. The married and the high school graduate were majority with 52.1% and 61.8% each. Current smokers were 44.7% and More than 50% of the participants drank alcohol sometimes. No exercise group was 59.3% and the participants who dissatisfied with their daily sleeping hours were 43.5%. In t-test and analysis of variance, the significant general characteristics associated with physical and psychological job stress were young age (p<0.001), single marital status (p<0.001), and short working period at the present company (p<0.001). The health behaviors related to physical job stress were current smoking, weight change during the past one year (p<0.001), weight control effort (p<0.001), exercise (p<0.001), daily sleeping dissatisfaction (p<0.001), break time, and perceived fatigue (p<0.001). All 10 health behavior factors were significantly associated with psychological job stress (p<0.05). Weight change, weight control effort, exercise, daily sleeping dissatisfaction, little break at work, and high perceived fatigue were significant factors affecting job stress. Daily sleeping dissatisfaction, little break at work, little exercise, weight change for the past one year and young age were selected as the significant health behavior and general factors affecting physical job stress symptoms in stepwise multiple regression analysis. The five factors explained 18.9% of the physical stress score variance. Six factors were selected as the significant health behaviors affecting psychological job stress: daily sleeping dissatisfaction, little exercise, frequent drinking alcohol, high perceived fatigue, little break at work, and little weight control effort. The six factors explained 10.6% of the psychological stress score variance.

• Journal of the Korean Data and Information Science Society
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• v.20 no.1
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• pp.1-9
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• 2009

Let us consider that the variance function in regression model has a discontinuity/change point at unknown location. Yu and Jones (2004) proposed the local polynomial fit to estimate the log-variance function which break the positivity of the variance. Using the local polynomial fit, Huh (2008) estimate the discontinuity point of the log-variance function. We propose a test for the existence of a discontinuity point in the log-variance function with the estimated jump size in Huh (2008). The proposed method is based on the asymptotic distribution of the estimated jump size. Numerical works demonstrate the performance of the method.

• Nuclear Engineering and Technology
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• v.48 no.3
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• pp.684-701
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• 2016

The Gaussian process model (GPM) is a flexible surrogate model that can be used for nonparametric regression for multivariate problems. A unique feature of the GPM is that a prediction variance is automatically provided with the regression function. In this paper, we estimate the safety margin of a nuclear power plant by performing regression on the output of best-estimate simulations of a large-break loss-of-coolant accident with sampling of safety system configuration, sequence timing, technical specifications, and thermal hydraulic parameter uncertainties. The key aspect of our approach is that the GPM regression is only performed on the dominant input variables, the safety injection flow rate and the delay time for AC powered pumps to start representing sequence timing uncertainty, providing a predictive model for the peak clad temperature during a reflood phase. Other uncertainties are interpreted as contributors to the measurement noise of the code output and are implicitly treated in the GPM in the noise variance term, providing local uncertainty bounds for the peak clad temperature. We discuss the applicability of the foregoing method to reduce the use of conservative assumptions in best estimate plus uncertainty (BEPU) and Level 1 probabilistic safety assessment (PSA) success criteria definitions while dealing with a large number of uncertainties.

• Journal of Korea Water Resources Association
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• v.42 no.7
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• pp.525-535
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• 2009

In this paper a heuristic method for identifying individual pipes in water pipe networks to determine specific sections of the pipes that need to be replaced due to deterioration. An appropriate minimum pipe length is determined by selecting the pipe length that has the greatest variance of the average cumulative break number slopes among the various pipe lengths used. As a result, the minimum pipe length for the case study water network is determined as 4 m and a total of 39 individual pipe IDs are obtained. The economically optimal replacement times of the individual pipe IDs are estimated by using the threshold break rate of an individual pipe ID and the pipe break trends models for which the General Pipe Break Prediction Model(Park and Loganathan, 2002) that can incorporate the linear, exponential, and in-between of the linear and exponetial failure trends and the ROCOFs based on the modified time scale(Park et al., 2007) are used. The maximum log-likelihoods of the log-linear ROCOF and Weibull ROCOF estimated for the break data of a pipe are compared and the ROCOF that has a greater likelihood is selected for the pipe of interest. The effects of the social costs of a pipe break on the optimal replacement time are also discussed.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.6
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• pp.9-16
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• 2020

This study aims to assess the effects of specimen thickness (ST) on fatigue crack growth in the early stages of crack propagation and near failure in magnesium alloys. The analysis of variance (ANOVA) method was adopted because fatigue crack propagation in magnesium alloys exhibits statistical behavior. The equality of variance test and residual diagnostics were performed on the grown cracks to confirm the validity of ANOVA by verifying the normal distribution and mutual independence of the residuals and their homoscedasticity. ANOVA confirmed that ST heavily impacts crack growth; i.e., when ST is smaller, cracks grow faster in the early crack propagation stage and break more quickly before the formation of larger cracks. We found that ST significantly affects fatigue crack growth in the early crack propagation stage and near the failure stage in magnesium alloys. The regression model was also used to predict crack formation near the failure stage.

• Journal of the Korean Operations Research and Management Science Society
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• v.40 no.4
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• pp.145-156
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• 2015

Incorporation of individual learning and forgetting behaviors within worker-task assignment models produces a mixed integer nonlinear program (MINLP) problem, which is difficult to solve as a NP hard due to its nonlinearity in the objective function. Previous studies commonly assume homogeneity among workers in workforce scheduling that takes account of learning and forgetting characteristics. This paper expands previous researches by considering heterogeneous individual learning/forgetting, and investigates the impact of worker heterogeneity in initial expertise, steady-state productivity, learning and forgetting on system performance to assist manager's decision-making in worker-task assignments without tackling complex MINLP models. In order to understand the performance implications of workforce heterogeneity, this paper examines analytically how heterogeneity in each of the four parameters of the exponential learning and forgetting (L/F) model affects system performance in three cases : consecutive assignments with no break, n breaks of s-length each, and total b break-periods occurred over T periods. The study presents the direction of change in worker performance under different assignment schedules as the variance in initial expertise, steady-state productivity, learning or forgetting increases. Thus, it implies whether having more heterogenous workforce in terms of each of four parameters in the L/F model is desired or not in different schedules from the perspective of system productivity measurement.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.993-996
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• 2005

High speed machining(HSM) technique is widely used in the appliance, automobile part and mold industries, which has many advantages such as good quality, low cost and rapid machining time. but it also has problems like tool break, smooth tool path, and so on. In particular, small size end mill is easy to break, so it must be changed before interrupting operation. Generally, the tool life of small size end mill is effected by the milling conditions whose evaluated parameters are spindle, feedrate, and width of cut. The experiments are carried out by full factorial design of experiments using and orthogonal array. This paper shows optimal combination and mathematical model for tool life, and the analysis of variance(ANOVA) is employed to analyze the main effects and the interactions of these milling parameters and the second-order polynomial regression model with three independent variables is estimated to predict tool life by multiple regression analysis.