• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.1
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• pp.101-112
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• 1994

The measurement of residual stress distribution of $Si_3N_4/SUS304$ joint was performed on 23 specimens with the same joint condition using PSPC type X-ray stress measurement system and the two-dimensional elastoplastic analysis using finite element method was also attempted. As results, residual stress distribution near the interface on the ceramic side of the joint was revealed quantitatively. Residual stress on the ceramic side of the joint was turned out to be tensional near the interface, maximum along the edge, varying in accordance with the condition of the joint and variance to be most conspicuous for the residual stress normal to the interface characterized by the stress singularities. In the vicinity of the interface, the high stress concentration occurs and residual stress distributes three-dimensionally. Therefore, the measured stress distribution differed remarkably from the result of the two-dimensional finite-element analysis. Especially at the center of the specimen near the interface, the residual stress, $\sigma_{x}$ obtained from the finite element analysis was compressive, whereas measurement using X-ray yielded tensile $\sigma_{x}$. Here we discuss two dimensional superposition model the discrepancy between the results from the two dimensional finite element analysis and X-ray measurement.

• Journal of the Korea Society for Simulation
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• v.10 no.4
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• pp.41-50
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• 2001

When optimizing a complex system by determining the optimum condition of the system parameters of interest, we often employ the process of estimating the unknown objective function, which is assumed to be a second order spline function. In doing so, we normally use common random numbers for different set of the controllable factors resulting in more accurate parameter estimation for the objective function. In this paper, we will show some mathematical result for the analysis of variance when using common random numbers in terms of the regression error, the residual error and the pure error terms. In fact, if we can realize the special structure of the covariance matrix of the error terms, we can use the result of analysis of variance for the uncorrelated experiments only by applying minor changes.

• The Korean Journal of Applied Statistics
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• v.5 no.2
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• pp.283-292
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• 1992

Updating algorithms are studied for the basic statistics (mean, variance). For a linear model, a recursive formulae for least squares estimators of regression coefficients, residual sum of squares and variance-covariance matrix are also studied. Hotelling's $T^2$ statistics can be calculated recursively using the recursive formulae of mean vector and variance-covariance matrix without computing the sample variance-covariance matrix at each stage.

• Communications for Statistical Applications and Methods
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• v.26 no.4
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• pp.337-346
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• 2019

This paper discusses a method for obtaining nonnegative estimates for variance components in a random effects model. A variance component should be positive by definition. Nevertheless, estimates of variance components are sometimes given as negative values, which is not desirable. The proposed method is based on two basic ideas. One is the identification of the orthogonal vector subspaces according to factors and the other is to ascertain the projection in each orthogonal vector subspace. Hence, an observation vector can be denoted by the sum of projections. The method suggested here always produces nonnegative estimates using projections. Hartley's synthesis is used for the calculation of expected values of quadratic forms. It also discusses how to set up a residual model for each projection.

• Journal of the Korean Data and Information Science Society
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• v.4
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• pp.97-107
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• 1993

In this paper we propose a nonparametric estimator of the men residual life function (MRLF) on a coherent system under the condition that the component lifetimes are censored by system lifetime. It is shown that the proposed estimator, considered as a function of age t, converges weakly to a Gaussian process on a fixed interval. A consistent estimator of asymptotic variance of the proposed estimator is also given.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.373-376
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• 2000

We investigate some properties of commonly used residual plots in linear regression and provide some systematic insight into the relationships among the plots. We discuss three issues of linear regression in this stream of context. First of all, we introduce two graphical comparison methods to display the variance inflation factor. Secondly, we show that the role of a suppressor variable in linear regression can be checked graphically. Finally, we show that several other types of standardized regression coefficients, besides the ordinary one, can be obtained in residual plots and the correlation coefficients of one of these residual plots can be used in ranking the relative importance of variables.

• Communications for Statistical Applications and Methods
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• v.7 no.1
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• pp.37-46
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• 2000

We investigate some properties of commonly used residual plots in linear regression and provide some systematic insight into the relationships among the plots. We discuss three issues of linear regression in this stream of context. First of all we introduce two graphical comparison methods to display the variance inflation factor. Secondly we show that the role of a suppressor variable in linear regression can be checked graphiclly. Finally we show that several other types of standardized regression coefficients besides the ordinary one can be obtained in residual plots and the correlation coefficients of one of these residual plots can be used in ranking the relative importance of variables.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.7
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• pp.1-15
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• 2022

Combining the strength of both Mixed Data Sampling (MIDAS) Regression and realized variance measures, this paper seeks to investigate two objectives: (1) evaluate the post-sample performance of the proposed weekly Realized Variance-MIDAS (RVar-MIDAS) in one-week ahead volatility forecasting against the established Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model and the less explored but robust STES (Smooth Transition Exponential Smoothing) methods. (2) comparing forecast error performance between realized variance and squared residuals measures as a proxy for actual volatility. Data of seven private equity mutual fund indices (generated from 57 individual funds) from two different time periods (with and without financial crisis) are applied to 21 models. Robustness of the post-sample volatility forecasting of all models is validated by the Model Confidence Set (MCS) Procedures and revealed: (1) The weekly RVar-MIDAS model emerged as the best model, outperformed the robust DAILY-STES methods, and the weekly DAILY-GARCH models, particularly during a volatile period. (2) models with realized variance measured in estimation and as a proxy for actual volatility outperformed those using squared residual. This study contributes an empirical approach to one-week ahead volatility forecasting of mutual funds return, which is less explored in past literature on financial volatility forecasting compared to stocks volatility.

• The Korean Journal of Applied Statistics
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• v.30 no.2
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• pp.259-269
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• 2017

Due to the nonnegativity of variance, most of nonparametric estimations of discontinuous variance function have used the Nadaraya-Watson estimation with residuals. By the modification of Chen et al. (2009) and Yu and Jones (2004), Huh (2014, 2016a) proposed the estimators of the log-variance function instead of the variance function using the local linear estimator which has no boundary effect. Huh (2016b) estimated the variance function using the adjusted squared residuals by the estimated jump size in the discontinuous variance function. In this paper, we propose an estimator of the discontinuous log-variance function using the local linear estimator with the adjusted log-squared residuals by the estimated jump size of log-variance function like Huh (2016b). The numerical work demonstrates the performance of the proposed method with simulated and real examples.

• Journal of the Korea Institute of Military Science and Technology
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• v.10 no.1
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• pp.14-21
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• 2007

This paper proposes SDINS(strapdown inertial navigation system) transfer alignment method for vertical launcher using an adaptive filter in the ship. First, the velocity and attitude matching transfer alignment method is designed to align SDINS for vertical launcher. Second, the adaptive filter is employed to estimate measurement noise variance in real time using the residual of measurements. Because it is difficult to decide measurement noise variance when noise properties of the ship SDINS are changed. To verify its performance, it is compared with the EKF(Extended Kalman filter) using uncorrect measurement variance. The monte carlo simulation results show that proposed method is more effective in estimating attitude angle than EKF.