• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.4
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• pp.597-602
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• 1987

In this paper, an adaptive observe based upon the exponentially weighted least-squares method is implemented in the design of a model reference adaptive controller for an unknown time-invariant discrete single-input single-output linear plant. A method of selecting the state variable filter is proposed. In this scheme, all the past data are weithted exponentially with the weighting coefficient.

• Journal of Korean Society for Quality Management
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• v.29 no.1
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• pp.11-23
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• 2001

This paper is concerned with suggesting a Bayesian method for variable selection in multinomial logit model. It is based upon an optimal rule suggested by use of Bayes rule which minimizes a risk induced by selecting the multinomial logit model. The rule is to find a subset of variables that maximizes the marginal likelihood of the model. We also propose a Laplace-Metropolis algorithm intended to suggest a simple method forestimating the marginal likelihood of the model. Based upon two examples, artificial data and empirical data examples, the Bayesian method is illustrated and its efficiency is examined.

• Journal of the Korean Data and Information Science Society
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• v.15 no.3
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• pp.605-616
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• 2004

In this paper we consider the proportional hazard models for survival analysis in the microarray data. For a given vector of response values and gene expressions (covariates), we address the issue of how to reduce the dimension by selecting the significant genes. In our approach, rather than fixing the number of selected genes, we will assign a prior distribution to this number. To implement our methodology, we use a Markov Chain Monte Carlo (MCMC) method.

• Journal of the Korean Data and Information Science Society
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• v.22 no.4
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• pp.795-801
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• 2011

In machine learning and statistics it is often the case that some variables are not important, while some variables are more important than others. We propose a novel algorithm for selecting such relevant variables in the kernel Cox regression. We employ the weighted version of ANOVA decomposition kernels to choose optimal subset of relevant variables in the kernel Cox regression. Experimental results are then presented which indicate the performance of the proposed method.

• The Korean Journal of Applied Statistics
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• v.17 no.3
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• pp.459-473
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• 2004

It has well known that an exhaustive search algorithm suggested by Breiman et. a1.(1984) has a trend to select the variable having relatively many possible splits as an splitting rule. We propose an algorithm to overcome this variable selection bias problem and then construct unbiased regression trees based on the algorithm. The proposed algorithm runs two steps of selecting a split variable and determining a split rule for binary split based on the split variable. Simulation studies were performed to compare the proposed algorithm with Breiman et a1.(1984)'s CART(Classification and Regression Tree) in terms of degree of variable selection bias, variable selection power, and MSE(Mean Squared Error). Also, we illustrate the proposed algorithm with real data sets.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.6
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• pp.868-875
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• 1995

This paper presents a method of constructing the multiple-valued combinational logic systems(MVCLS) by decision diagram. The switching function truth table of MVCLS is transformed into canonical normal form of sum-of-products(SOP) with literals at first. Next, the canonical normal form of SOP is transfered into multiple-valued logic decision diagram(MVLDD). The selecting of variable ordering is very important in this stage. The MVLDDs are quite different from each other according to the variable ordering. Sometimes the inadequate variable ordering produces a very large size of MVLDD means the large size of circuit implementation. An algorithm for generating the proper variable ordering produce minimal MVLDD and an example shows the verity of the algorithm. The circuits are realized with T-gate acceording to the minimal MVLDD.

• Journal of the Korean Data and Information Science Society
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• v.24 no.1
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• pp.201-209
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• 2013

For censored regression, it is often the case that some input variables are not important, while some input variables are more important than others. We propose a novel algorithm for selecting such important input variables for censored kernel regression, which is based on the penalized regression with the weighted quadratic loss function for the censored data, where the weight is computed from the empirical survival function of the censoring variable. We employ the weighted version of ANOVA decomposition kernels to choose optimal subset of important input variables. Experimental results are then presented which indicate the performance of the proposed variable selection method.

• Communications for Statistical Applications and Methods
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• v.26 no.1
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• pp.69-78
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• 2019

A structured model with both single-index and varying coefficients is a powerful tool in modeling high dimensional data. It has been widely used because the single-index can overcome the curse of dimensionality and varying coefficients can allow nonlinear interaction effects in the model. For high dimensional index vectors, variable selection becomes an important question in the model building process. In this paper, we propose an efficient estimation and a variable selection method based on a smoothing spline approach in a partially linear single-index-coefficient regression model. We also propose an efficient algorithm for simultaneously estimating the coefficient functions in a data-adaptive lower-dimensional approximation space and selecting significant variables in the index with the adaptive LASSO penalty. The empirical performance of the proposed method is illustrated with simulated and real data examples.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.2
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• pp.191-196
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• 2015

There have been many studies on statistical forecasting on firm's performance and stock price by applying various financial indicators such as debt ratio and sales growth rate. Selecting predictors for constructing a prediction model among the various financial indicators is very important for precise prediction. Most of the previous studies applied variable selection algorithms for selecting predictors. However, the variable selection algorithm is considered to be at risk of eliminating certain amount of information from the indicators that were excluded from model construction. Therefore, we propose a firm's performance prediction model which principal component analysis is applied instead of the variable selection algorithm, in order to reduce dimensionality of input variables of the prediction model. In this study, we constructed the proposed prediction model by using financial data of American IT companies to empirically analyze prediction performance of the model.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.4C
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• pp.185-190
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• 2005

In IS-95 and IMT-2000 systems using variable code rates and constraint lengths, this paper limits code rate 1/2 and constraint length 3 and realizes forward real-time decoder using Sliding-Window with decoding length 6 and PVSL(Prototype Vector Selecting Logic), LVQ(Learning Vector Quantization) in Neural Network. In comparison condition to theoretically constrained AWGN channel environment at $S/(N_{0}/2)=1$ I verified the superiority of forward real-time decoder through hard-decision and soft-decision comparison between Viterbi decoder and forward real-time decoder such as BER and Secure Communication and H/W Structure.