• Journal of the Korean Statistical Society
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• v.33 no.4
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• pp.411-433
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• 2004

The shrinkage preliminary test ridge regression estimators (SPTRRE) based on Wald (W), Likelihood Ratio (LR) and Lagrangian Multiplier (LM) tests for estimating the regression parameters of the multiple linear regression model with multivariate Student's t error distribution are considered in this paper. The quadratic biases and risks of the proposed estimators are compared under both null and alternative hypotheses. It is observed that there is conflict among the three estimators with respect to their risks because of certain inequalities that exist among the test statistics. In the neighborhood of the restriction, the SPTRRE based on LM test has the smallest risk followed by the estimators based on LR and W tests. However, the SPTRRE based on W test performs the best followed by the LR and LM based estimators when the parameters move away from the subspace of the restrictions. Some tables for the maximum and minimum guaranteed efficiency of the proposed estimators have been given, which allow us to determine the optimum level of significance corresponding to the optimum estimator among proposed estimators. It is evident that in the choice of the smallest significance level to yield the best estimator the SPTRRE based on Wald test dominates the other two estimators.

• Journal of the Korean Society of Clothing and Textiles
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• v.11 no.1
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• pp.1-10
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• 1987

We selected four kinds of cotton velvet and chafed before and after cleaning, then in accordance with abrasion times we measured of pile exclusion rate and examined the relationship of the pile exclusion rate, its thickness and the air permeability. An experimental study was carried out the pile weave construction, the density, the yarn to yarn, the shrinkage, and the pile substantiality. The results were as follows: 1. In accordance with increments of shrinkage phenomenon of pile fabric for cleaning process, pile exclusion rate was decreased. 2. The ground weave of pile fabric and the yarn to yarn of warp and weft direction were affected by the pile exclusion. 3. It is linear of pile substantiality of pile fabric and pile fastness. 4. In estimation of pile exclusion rate, it is proper to make use of air permeability and measuring value of thickness.

• 한국데이터정보과학회:학술대회논문집
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• 2005.04a
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• pp.83-126
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• 2005

Single-index models have found applications in econometrics and biometrics, where multidimensional regression models are often encountered. Here we propose a nonparametric estimation approach that combines wavelet methods for non-equispaced designs with Bayesian models. We consider a wavelet series expansion of the unknown regression function and set prior distributions for the wavelet coefficients and the other model parameters. To ensure model identifiability, the direction parameter is represented via its polar coordinates. We employ ad hoc hierarchical mixture priors that perform shrinkage on wavelet coefficients and use Markov chain Monte Carlo methods for a posteriori inference. We investigate an independence-type Metropolis-Hastings algorithm to produce samples for the direction parameter. Our method leads to simultaneous estimates of the link function and of the index parameters. We present results on both simulated and real data, where we look at comparisons with other methods.

• Structural Engineering and Mechanics
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• v.36 no.2
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• pp.165-179
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• 2010

Maximum deflection in a beam is a design criteria and occurs generally at or close to the mid-span. Neural networks have been developed for the continuous composite beams to predict the inelastic mid-span deflections (typically for 20 years, considering cracking, and time effects, i.e., creep and shrinkage, in concrete) from the elastic moments and elastic mid-span deflections (neglecting instantaneous cracking and time effects). The training and testing data for the neural networks is generated using a hybrid analytical-numerical procedure of analysis. The neural networks have been validated for four example beams and the errors are shown to be small. This methodology, of using networks enables a rapid estimation of inelastic mid-span deflections and requires a computational effort almost equal to that required for the simple elastic analysis. The neural networks can be extended for the composite building frames that would result in huge saving in computational time.

• Journal of Korean Society for Quality Management
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• v.30 no.2
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• pp.86-98
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• 2002

Defining the relationship between the quality of injection molded parts and the process condition is very complicate because of lots of factor are involved and each factor has a non-linearity. With the development of CAE(Computer Aided Engineering) technology, the estimation of volumetric shrinkage of injection mold parts is possible by computer simulation even though restricted application. In this research, Neural Network applied for finding optimal processing condition. The percent of volumetric shrinkage compared on each case and show neural network can be successfully applied selecting optimum condition not only within factor level but also between factor level.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.18 no.2
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• pp.143-156
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• 2014

This paper aims to give a quick view on denoising without comprehensive details. Denoising can be understood as removing unwanted parts in signals and images. Noise incorporates intrinsic random fluctuations in the data. Since noise is ubiquitous, denoising methods and models are diverse. Starting from what noise means, we briefly discuss a denoising model as maximum a posteriori estimation and relate it with a variational form or energy model. After that we present a few major branches in image and signal processing; filtering, shrinkage or thresholding, regularization and data adapted methods, although it may not be a general way of classifying denoising methods.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.25 no.2
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• pp.71-76
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• 2002

Defining the relationship between the quality of Injection molded parts and the process condition is very complicate because of lots of factors are involved and each factor has a non-linearity. With the development of CAE(Computer Aided Engineering) technology, the estimation of volumetric shrinkage of injection mold parts is possible by computer simulation in spite of restricted application. In this research, the Taguchi method md Neural Network are applied for finding optimal processing condition. The percent of volumetric shrinkage is compared on each case and shows neural network can be successfully applied.

• Journal of the Korean Data and Information Science Society
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• v.24 no.5
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• pp.1077-1088
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• 2013

High-dimensional data analysis arises from almost all scientific areas, evolving with development of computing skills, and has encouraged penalized estimations that play important roles in statistical learning. For the past years, various penalized estimations have been developed, and the least absolute shrinkage and selection operator (LASSO) proposed by Tibshirani (1996) has shown outstanding ability, earning the first place on the development of penalized estimation. In this paper, we first introduce a number of recent advances in high-dimensional data analysis using the LASSO. The topics include various statistical problems such as variable selection and grouped or structured variable selection under sparse high-dimensional linear regression models. Several unsupervised learning methods including inverse covariance matrix estimation are presented. In addition, we address further studies on new applications which may establish a guideline on how to use the LASSO for statistical challenges of high-dimensional data analysis.

• Proceedings of the Korea Concrete Institute Conference
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• 2001.05a
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• pp.903-908
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• 2001

This paper deals with the estimation of the maximum flexural crack widths using minimum crack spacing for reinforced concrete members. The proposed method utilizes the conventional crack and bond-slip theories as well as bonding transfer length and effects of creep and shrinkage between the reinforcement and concrete. An analytical equation for the estimation of the maximum flexural crack width is formulated as a function of mean bond stress. The validity, accuracy and efficiency of the proposed method are established by comparing the analytical results with the experimental data and the major code specifications (e.g., ACI, CEB-FIP Model code, Eurocode 2, etc.). The analytical results of analysis presented in this paper indicate that the proposed method can be effectively estimated the maximum flexural crack width of the reinforced concrete members.

• Proceedings of the Korea Concrete Institute Conference
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• 2008.11a
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• pp.245-248
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• 2008

Concrete structures show time-dependent behavior due to creep and shrinkage of concrete and the uncertainties of creep and shrinkage are very huge. To reduce uncertainties of creep and shrinkage, it is substantially necessary to perform the long-term creep and shrinkage tests, but actual construction process doesn't allow it due to the limited time. Even though the tests are performed in laboratory, the values obtained from the tests could be different from the actual values in construction site because of the different environment between the laboratory and construction site and the model uncertainty itself. It is difficult to predict the long-term behaviors of concrete structures properly if the assumed creep coefficient obtained from Codes or the results of experiments is different from the real characteristics of concrete creep. In this study, for predicting the long-term behavior, the creep coefficients in reinforced concrete beams are estimated using creep sensitivity analysis from the measured deflections with time. And estimated creep coefficients using creep models of ACI Committee 209 and CEB-FIP MC90 are compared.