• Communications for Statistical Applications and Methods
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• v.7 no.1
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• pp.165-178
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• 2000

Bayesian methods are considered for the multiple caputure-recapture data. Reference priors are developed for such model and sampling-based approach through Gibbs sampler is used for inference from posterior distributions. Furthermore approximate Bayes factors are obtained for model selection between trap and nontrap response models. Finally one methodology is implemented for a capture-recapture model in generated data and real data.

• Journal of the Korean Statistical Society
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• v.33 no.3
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• pp.255-269
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• 2004

This paper develops log-density estimation for directional data. The methodology is to use expansions with respect to spherical harmonics followed by estimating the unknown parameters by maximum likelihood. Minimax rates of convergence in terms of the Kullback-Leibler information divergence are obtained.

• Journal of the Korean Statistical Society
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• v.36 no.4
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• pp.557-578
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• 2007

In Response Surface Methodology (RSM), rotatability is a natural and highly desirable property. For second order general correlated regression model, the concept of robust rotatability was introduced by Das (1997). In this paper a new measure of robust rotatability for second order response surface designs with correlated errors is developed and illustrated with an example. A comparison is made between the newly developed measure with the previously suggested measure by Das (1999).

• Communications for Statistical Applications and Methods
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• v.11 no.3
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• pp.563-574
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• 2004

Slope rotatability of response surface designs is a desirable property when we are interested in estimating slopes of response surfaces. In this paper, we derive a simplified measure of slope rotatability from new viewpoints for response surface designs that are frequently used in response surface methodology.

• Applied Biological Chemistry
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• v.47 no.1
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• pp.96-106
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• 2004

Response surface methodology (RSM) was applied in roasting processes of perilla leaves to develop a high quality perilla leaf tea. The Hunter color parameters and electron donating ability were monitored to optimize organoleptic properties of perilla leaf tea. The roasting processes were based on the central composite design with primary variables-roasting temperature $(140{\sim}220^{\circ}C)$, time $(5{\sim}25)$, and reaction variables-sensory test, electron donating ability. From the variables, the roasting condition was optimized using statistical analysis system (SAS) program as developing the functional tea using perilla leaf. Hunter color L and b values of the powdered samples increased with the roasting processes, but Hunter color a value decreased. Electron donating ability was influenced by roasting temperature (p<0.01) and time (p<0.01), and optimum condition selected was at $220^{\circ}C$ for 15 min with coefficient of determinations $(R^2)$ above 0.98. After preference test of perilla leaf tea using parameter of taste, color, and flavor, we can estimate that the optimal roasting condition of preilla leaf for function tea manufacturing are $210{\sim}220^{\circ}C$ for $10{\sim}20$ min by response surface methodology (RSM). Tyrosinase, xanthine oxidase and electron donating ability were 10.14, 14.37 and 59.19% of perilla leaf tea.

• Communications for Statistical Applications and Methods
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• v.20 no.2
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• pp.147-155
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• 2013

To determine whether each coefficient is equal to zero or not, usual $t$-tests are a popular choice (among others) in linear regression to practitioners because all statistical packages provide the statistics and their corresponding $p$-values. Under smaller samples (especially with non-normal errors) the tests often fail to correctly detect statistical significance. We propose a permutation approach by adopting a sufficient dimension reduction methodology to overcome this deficit. Numerical studies confirm that the proposed method has potential advantages over the t-tests. In addition, data analysis is also presented.

• IE interfaces
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• v.17 no.1
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• pp.1-12
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• 2004

In a modern semiconductor device manufacturing industry, statistical bin limits on wafer level test bin data are used for minimizing value added to defective product as well as protecting end customers from potential quality and reliability excursion. Most wafer level test bin data show skewed distributions. By Monte Carlo simulation, this paper evaluates methods and sample size effect regarding determination of statistical bin limits. In the simulation, it is assumed that wafer level test bin data follow the Poisson distribution. Hence, typical shapes of the data distribution can be specified in terms of the distribution's parameter. This study examines three different methods; 1) percentile based methodology; 2) data transformation; and 3) Poisson model fitting. The mean square error is adopted as a performance measure for each simulation scenario. Then, a case study is presented. Results show that the percentile and transformation based methods give more stable statistical bin limits associated with the real dataset. However, with highly skewed distributions, the transformation based method should be used with caution in determining statistical bin limits. When the data are well fitted to a certain probability distribution, the model fitting approach can be used in the determination. As for the sample size effect, the mean square error seems to reduce exponentially according to the sample size.

• Journal of Microbiology and Biotechnology
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• v.23 no.12
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• pp.1757-1764
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• 2013

Trichoderma harzianum ZF-2 producing laccase was isolated from decaying samples from Shandong, China, and showed dye decolorization activities. The objective of this study was to optimize its culture conditions using a statistical analysis of its laccase production. The interactions between different fermentation parameters for laccase production were characterized using a Plackett-Burman design and the response surface methodology. The different media components were initially optimized using the conventional one-factor-at-a-time method and an orthogonal test design, and a Plackett-Burman experiment was then performed to evaluate the effects on laccase production. Wheat straw powder, soybean meal, and $CuSO_4$ were all found to have a significant influence on laccase production, and the optimal concentrations of these three factors were then sequentially investigated using the response surface methodology with a central composite design. The resulting optimal medium components for laccase production were determined as follows: wheat straw powder 7.63 g/l, soybean meal 23.07 g/l, $(NH_4)_2SO_4$ 1 g/l, $CuSO_4$ 0.51 g/l, Tween-20 1 g/l, $MgSO_4$ 1 g/l, and $KH_2PO_4$ 0.6 g/l. Using this optimized fermentation method, the yield of laccase was increased 59.68 times to 67.258 U/ml compared with the laccase production with an unoptimized medium. This is the first report on the statistical optimization of laccase production by Trichoderma harzianum ZF-2.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.1
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• pp.67-75
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• 2011

This paper presents a new test technique for evaluating performance of vehicle detectors with interval estimation, not the conventional point estimation, for presenting statistical confidence interval. The methodology is categorized into three parts; sampling plan, analysis on the characteristic of evaluation indices, and the expression of evaluation results. Even though many statistical sampling plans exist, stratified random sampling is regarded as the most appropriate one, considering the detector performance characteristics that varies with traffic, illumination, and meteorological conditions. No magic bullet exists for evaluation index for detector evaluation, hence the characteristics of evaluation indices were thoroughly analyzed and a reasonable process for choosing the best evaluation index is proposed. Finally, the methodology to express the result of detector evaluation for the entire evaluation period and individual analysis interval is represented, respectively. To overcome the existing drawbacks in point estimation, interval estimation by which statistical confidence interval can be represented is introduced for enhancing statistical reliability of traffic detector evaluation. This research can make vehicle detector scheme improve one step forward.

• Geophysics and Geophysical Exploration
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• v.1 no.3
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• pp.170-175
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• 1998

A systematic methodology is developed for the prediction of the lithology using electrofacies classification from wireline log data. Multivariate statistical techniques are adopted to segment well log measurements and group the segments into electrofacies types. To consider corresponding contribution of each log and reduce the computational dimension, multivariate logs are transformed into a single variable through principal components analysis. Resultant principal components logs are segmented using the statistical zonation method to enhance the quality and efficiency of the interpreted results. Hierarchical cluster analysis is then used to group the segments into electrofacies. Optimal number of groups is determined on the basis of the ratio of within-group variance to total variance and core data. This technique is applied to the wells in the Korea Continental Shelf. The results of field application demonstrate that the prediction of lithology based on the electrofacies classification works well with reliability to the core and cutting data. This methodology for electrofacies determination can be used to define reservoir characterization which is helpful to the reservoir management.