• Communications for Statistical Applications and Methods
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• v.31 no.2
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• pp.179-189
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• 2024

In multivariate regression with a high-dimensional response Y ∈ ℝ^r and a relatively low-dimensional predictor X ∈ ℝ^p (where r ≥ 2), the statistical analysis of such data presents significant challenges due to the exponential increase in the number of parameters as the dimension of the response grows. Most existing dimension reduction techniques primarily focus on reducing the dimension of the predictors (X), not the dimension of the response variable (Y). Yoo and Cook (2008) introduced a response dimension reduction method that preserves information about the conditional mean E(Y | X). Building upon this foundational work, Yoo (2018) proposed two semi-parametric methods, principal response reduction (PRR) and principal fitted response reduction (PFRR), then expanded these methods to unstructured principal fitted response reduction (UPFRR) (Yoo, 2019). This paper reviews these four response dimension reduction methodologies mentioned above. In addition, it introduces the implementation of the mbrdr package in R. The mbrdr is a unique tool in the R community, as it is specifically designed for response dimension reduction, setting it apart from existing dimension reduction packages that focus solely on predictors.

• Proceedings of the International Microelectronics And Packaging Society Conference
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• 2001.11a
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• pp.119-123
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• 2001

In this paper, a new structure and fabrication method for the wafer level package(WLP) is presented. A packaged VLSI chip is encapsulated by a parylene(which is a low k material) layer as a dielectric layer and is molded by SUB photo-epoxy with dielectric constant of 3.0 at 100 MHz. The electrical parameters (R, L, C) of package traces are extracted by using the Maxwell 3-D simulator. Based on HSPICE simulation results, the proposed wafer level package can operate for frequencies up to 20GHz.

• Proceedings of the International Microelectronics And Packaging Society Conference
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• 2000.04a
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• pp.3-7
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• 2000

In summary, a fundamentally new paradigm called System-on-Package could potentially become a complementary alternative to System-on-Chip, thus providing a balanced set of system-level functions between the semiconductor IC and single component package beyond the year 2007. The concurrent engineering and optimization of IC and package could overcome the fundamental IC issues presented above.

• Knowledge Management Research
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• v.20 no.2
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• pp.1-24
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• 2019

This tutorial introduces procedures and methods for performing structural equation modeling using R. To do this, we present advanced analysis methods based on structural equation model such as mediation effect analysis, moderation effect analysis, moderated mediation effect analysis, and multiple-group analysis with R program code using R lavaan package that supports structural equation modeling. R is flexible and scalable, unlike traditional commercial statistical packages. Therefore, new analytical techniques are likely to be implemented ahead of any other statistical package. From this point of view, R will be a very appropriate choice for applying new analytical techniques or advanced techniques that researchers need. Considering that various studies in the social sciences are applying structural equations modeling techniques and increasing interest in open source R, this tutorial is expected to be useful for researchers who are looking for alternatives to existing commercial statistical packages.

• Journal of the Korean Data and Information Science Society
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• v.24 no.4
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• pp.931-940
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• 2013

For analysis of clustered survival data, the inferences of parameters in semi-parametric frailty models have been widely studied. It is also important to investigate the potential heterogeneity in event times among clusters (e.g. centers, patients). For purpose of this analysis, the interval estimation of frailty is useful. In this paper we propose a visualizing method to present confidence intervals of individual frailties across clusters using the frailtyHL R-package, which is implemented from h-likelihood methods for frailty models. The proposed method is demonstrated using two practical examples.

• Geophysics and Geophysical Exploration
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• v.11 no.4
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• pp.379-384
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• 2008

R is a free software for statical computing and graphics. It compiles and runs not only on UNIX platforms but MS Windows. The R commands are easy and offer interactive help. R is used in extensive field by implementing packages. RSEIS, the package of R, enable us to do easy graphic process of seismic data. Here we illustrate an example of the seismic data process using RSEIS.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.8
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• pp.883-888
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• 2014

Simulation is now using in various area as an effective decision analysis tool in complex environment of today. But, There is a focus to the simulation model development and execution better than result analysis. This article will emphasis to the importance of result analysis apart from model development in simulation, and will use R package for profit optimization simulation. R has a various function in statistic analysis and data manipulation, graphic display. So this research can show the value of R as a tool for simulation.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.05a
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• pp.177-182
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• 2004

Electronic products are subjected to many different types of shock environment. As the Optical Disc Drive (ODD) market grows, the number of failures related to shock increases. Therefore, it is necessary to improve the performance of cushion package as well as the product design. Cushion materials such as expanded polystyrene are often used to protect electronic products from shock environment. In this paper, the drop analysis of the cushion package f3r optical disc drives was carried out with the explicit method of LS-DYNA and verified by the drop test. For the optimization of package, response surface approximation model was created using central composite design. As a result, cushioning performance was improved under the critical condition and practical design guidelines of cushion package were suggested.

• Journal of the Korean Data and Information Science Society
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• v.28 no.5
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• pp.1001-1019
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• 2017

The signal processing is a field of the electrical engineering but it is very much related with the time series analysis. Thesedays the commercial softwares are widely used by the reseachers. We have attempted to make a guide for using the R-package in the digital signal processing. It would be good to read the materials in each section first and to follow the plots in the section 8 and to run the attached R-codes. The article consists of (1) Fourier transform and Fourier inverse transform, (2) spectral analysis (3) parametric and non-parametric estimation for the period (4) filter design. Simple theoretical explanations are provided and R implementations are added.

• Communications for Statistical Applications and Methods
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• v.18 no.3
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• pp.319-331
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• 2011

In this paper, we propose automatic procedures for the model selection of various univariate time series data. Automatic model selection is important, especially in data mining with large number of time series, for example, the number (in thousands) of signals accessing a web server during a specific time period. Several methods have been proposed for automatic model selection of time series. However, most existing methods focus on linear time series models such as exponential smoothing and autoregressive integrated moving average(ARIMA) models. The key feature that distinguishes the proposed procedures from previous approaches is that the former can be used for both linear time series models and nonlinear time series models such as threshold autoregressive(TAR) models and autoregressive moving average-generalized autoregressive conditional heteroscedasticity(ARMA-GARCH) models. The proposed methods select a model from among the various models in the prediction error sense. We also provide an R package autots that implements the proposed automatic model selection procedures. In this paper, we illustrate these algorithms with the artificial and real data, and describe the implementation of the autots package for R.