In this paper we propose how to simultaneously optimize multiple responses for robust design when data are collected from a combined array. The proposed method is based on the quadratic loss function. An example is illustrated to show the proposed method.

Consider a stuffing process where sausage-casings are filled with sausage-kneading. One of the most important factors in the stuffing process is weights of stuffed sausages. Sausages weighting above the specified limit are sold in a regular market price for a fixed price, and underfilled sausages are reworked at the expense of reprocessing cost. In this paper, the sausage stuffing process is inspected for improving productivity and quality levels. Several statistical process control tools are suggested by using real data obtained from a Korean Vienna sausage company.

The methods of data mining are decision tree, association rules, clustering, neural network and so on. Data mining is the method to find useful information for large amounts of data in database. It is used to find hidden knowledge by massive data, unexpectedly pattern, relation to new rule. We analyze Gyeongnam social indicator survey data by 2003 using association rule technique for environment information. Association rules are useful for determining correlations between attributes of a relation and have applications in marketing, financial and retail sectors. We can use association rule outputs in environmental preservation and environmental improvement.

We analyze an M/G/1 queueing system under $P_{\lambda,\tau}^M$ service policy. By using the level crossing theory and solving the corresponding integral equations, we obtain the stationary distribution of the workload in the system explicitly.

한우 17번 염색체 유전자 지도에서 QTL (quantitative trait loci) 분석을 실시하여 선별된 Loci 값들을 순열검정(Permutation Test)을 이용하여 유의성 검정을 실시하였다. 한편, 우수 경제형질 DNA marker들을 K-평균 군집법을 실시 파악하였다. 또한, 부스트랩 방법을 이용하여 선별된 Locus의 DNA Marker들의 신뢰구간을 구하였다. 이들 QTL과 K-평균법, 부스트랩 방법에 의해 한우의 염색체 17번 BMS941의 우수 DNA Marker 85, 105번을 선별하였다.

We consider the problem of estimating the scale and shape parameter of the Weibull distribution based on censored samples. we propose the approximate maximum likelihood estimators (AMLEs) of the scale and shape parameters in the Weibull distribution based on Type-II censored samples. We compare the proposed estimators in the sense of mean squared error (MSE).

Factorial experiments are studied in this paper. The Designs, thus, have factorial balance with respect to estimable main effects and interactions. John and Lewis (1983) considered generalized cyclic row-column designs for factorial experiments. A simple method of constructing confounded designs using the classical method of confounding for block designs is described in this paper.

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.

A suitable representation for the conceptual simplicity of the data in statistics and signal processing is essential for a subsequent analysis such as prediction, pattern recognition, and spatial analysis. Independent component analysis (ICA) is a statistical method for transforming an observed high-dimensional multivariate data into statistically independent components. ICA has been applied increasingly in wide fields of spectrum application since ICA is able to extract unknown components of a mixture from spectra. We focus on application of ICA for separating independent sources and predicting each composition using extracted components. The theory of ICA is introduced and an application to a metal surface spectra data will be described, where subsequent analysis using non-negative least square method is performed to predict composition ratio of each sample. Furthermore, some simulation experiments are performed to demonstrate the performance of the proposed approach.

In this paper, we develop the noninformative priors for the exponential models when the parameter of interest is the difference of two means. We develop the first and second order matching priors. We reveal that the second order matching priors do not exist. It turns out that Jeffreys' prior does not satisfy a first order matching criterion. The Bayesian credible intervals based on the first order matching meet the frequentist target coverage probabilities much better than the frequentist intervals of Jeffreys' prior.

There have been some controversies on the use of the coefficient of determination for linear no-intercept model. One definition of the coefficient of determination, $R^2=\sum\;{y}{^{\hat{2}}/\sum\;{y^2}$, is being widely accepted only for linear no-intercept models though Kvalseth(1985) demonstrated some possible pitfalls in using such $R^2$. Main objective of this article is to provide a cautionary notice for use of the $R^2$ by pointing out its tricky aspects by means of empirical simulations.

This paper applies Support Vector Regression (SVR) to estimate and forecast nonlinear autoregressive integrated (ARI) model of the daily exchange rates of four currencies (Swiss Francs, Indian Rupees, South Korean Won and Philippines Pesos) against U.S. dollar. The forecasting abilities of SVR are compared with linear ARI model which is estimated by OLS. Sensitivity of SVR results are also examined to kernel type and other free parameters. Empirical findings are in favor of SVR. SVR method forecasts exchange rate level better than linear ARI model and also has superior ability in forecasting the exchange rates direction in short test phase but has similar performance with OLS when forecasting the turning points in long test phase.

Data mining is the method to find useful information for large amounts of data in database. It is used to find hidden knowledge by massive data, unexpectedly pattern, relation to new rule. The methods of data mining are decision tree, association rules, clustering, neural network and so on. The decision tree approach is most useful in classification problems and to divide the search space into rectangular regions. Decision tree algorithms are used extensively for data mining in many domains such as retail target marketing, fraud detection, data reduction and variable screening, category merging, etc. We analyze waste database united with local information using decision tree techniques for environmental information. We can use these decision tree outputs for environmental preservation and improvement.

소규모의 사회복지 시설들은 대규모의 전문병원들이 갖춘 고 부가가치 전산 시스템들을 갖추고 있지 못하다. 그러므로 소규모의 사회복지시설들은 관리 및 서비스의 질적 향상을 기대할 수 없다. 이로 인해 경영관리와 서비스 차원에서 차이가 발생한다. 소규모에서 관리과 서비스의 향상을 위해 본 연구에서는 소규모 사회복지시설(Silver-town)에 적합한 통합 의료정보 데이터베이스시스템을 설계하여 제안하고자 한다.

In this study, we propose a chi-squared test of spherical symmetry. The advantage of this test is that the test statistic and its asymptotic p-value are easy to compute. A simulation study is conducted to study the accuracy, in finite samples, of the limiting distribution of the test statistic under spherical symmetry. The power of our test is compared with those of other tests for spherical symmetry in various alternative distributions via simulation.

There are many data mining techniques such as association rule, decision tree, neural network analysis, clustering, genetic algorithm, bayesian network, memory-based reasoning, etc. We analyze 2003 Gyeongnam social indicator survey data using k-means clustering technique for environmental information. Clustering is the process of grouping the data into clusters so that objects within a cluster have high similarity in comparison to one another. In this paper, we used k-means clustering of several clustering techniques. The k-means clustering is classified as a partitional clustering method. We can apply k-means clustering outputs to environmental preservation and environmental improvement.

A simple method is proposed to detect the number of change points with jump discontinuities in nonparamteric regression functions. The proposed estimators are based on a local linear regression fit by the comparison of left and right one-side kernel smoother. Also, the proposed methodology is suggested as the test statistic for detecting of change points and the direction of jump discontinuities.