• Communications for Statistical Applications and Methods
• /
• v.11 no.2
• /
• pp.235-246
• /
• 2004

• The Korean Journal of Applied Statistics
• /
• v.27 no.6
• /
• pp.1029-1038
• /
• 2014

Bayesian experimental design is a useful concept in applied statistics for the design of efficient experiments especially if prior knowledge in the experiment is available. However, a theoretical or numerical approach is not simple to implement. We review the concept of a Bayesian experiment approach for linear and nonlinear statistical models. We investigate relationships between prior knowledge and optimal design to identify Bayesian experimental design process characteristics. A balanced design is important if we do not have prior knowledge; however, prior knowledge is important in design and expert opinions should reflect an efficient analysis. Care should be taken if we set a small sample size with a vague improper prior since both Bayesian design and non-Bayesian design provide incorrect solutions.

• Proceedings of the Safety Management and Science Conference
• /
• 2012.04a
• /
• pp.579-583
• /
• 2012

The Design of Experiment (DOE) is a most practical technique when establishing an optimal condition for production technology in Six Sigma innovation project. This research proposes the assessment of properties of error terms, such as normality, equal variance, unbiasedness and independence. The properties of six nonparametric ranking techniques for checking normality assumption are discussed as well as run test which is used to identify the randomness, and to check unbiased assumption. Furthermore, Durbin-Watson (DW) statistics and ARIMA (p,d,q) process are discussed to identify the serial correlation.

• Proceedings of the CALSEC Conference
• /
• 2001.08a
• /
• pp.637-649
• /
• 2001

ㆍ Research Method - Experimental Design ㆍWhen Entity Bean is deployed and Client request to inquire a specific information of Doctor Table, we experiment Total Time for Query Execution according to Time Measurement Operation in Client code. ㆍWe can apply the statistics for the experiment to the design of Entity Beans.(omitted)

• Proceedings of the Korean Society for Bioinformatics Conference
• /
• 2004.11a
• /
• pp.272-278
• /
• 2004

Recently, Pepe et al. (2003) employed the receiver operating characteristic (ROC) approach to rank candidate genes from a microarray experiment that can be used for the biomarker development with the ultimate purpose of the population screening of a cancer, In the cancer microarray experiment based on n patients the researcher often wants to compare the tumor tissue with the normal tissue within the same individual using a common reference RNA. This design is referred to as a reference design or an indirect design. Ideally, this experiment produces n pairs of microarray data, where each pair consists of two sets of microarray data resulting from reference versus normal tissue and reference versus tumor tissue hybridizations. However, for certain individuals either normal tissue or tumor tissue is not large enough for the experimenter to extract enough RNA for conducting the microarray experiment, hence there are missing values either in the normal or tumor tissue data. Practically, we have $n_1$ pairs of complete observations, $n_2$ 'normal only' and $n_3$ 'tumor only' data for the microarray experiment with n patients, where n=$n_1$+$n_2$+$n_3$. We refer to this data set as a mixed data set, as it contains a mix of fully observed and partially observed pair data. This mixed data set was actually observed in the microarray experiment based on human tissues, where human tissues were obtained during the surgical operations of cancer patients. Pepe et al. (2003) provide the rationale of using ROC approach based on two independent samples for ranking candidate gene instead of using t or Mann -Whitney statistics. We first modify ROC approach of ranking genes to a paired data set and further extend it to a mixed data set by taking a weighted average of two ROC values obtained by the paired data set and two independent data sets.

• Journal of Microbiology and Biotechnology
• /
• v.15 no.6
• /
• pp.1377-1383
• /
• 2005

In a cDNA microarray experiment using Cy3 and Cy5 as labeling agents, particularly for the direct design, cDNAs from some genes incorporate one dye more efficiently than the other, which is referred to as the gene-specific dye bias. Dye-swaps, in which two dyes are switched on replicate arrays, are commonly used to control the gene-specific dye bias. We developed a simple procedure to extract the gene-specific dye bias information from a partial dye swap experiment. We detected gene-specific dye bias by identifying outliers in an X-Y plane, where the X axis represents the average log-ratio from two sets of dye swap pairs and the Y axis exhibits the average log ratio of four forward labeled arrays. We used this information for detecting differentially expressed genes, of which the additionally detected genes were validated by real-time RT-PCR.

• Journal of the Korean Statistical Society
• /
• v.29 no.2
• /
• pp.219-229
• /
• 2000

Oh et al.(1999) 3$\times$2 crossover design for assessing bioequivalence when two new generic drug formulations and innovator are simultaneously considered. This design is not only more efficient than 3$\times$3 one, proposed by Lee et al.(1998), in practical sense, but also more ethical in medical sense. However, the general statistical methods are not directly applicable to both designs when subjects are dropped out in the experiment, even though it is always possible in bioavailability and bioequivalence studies because of some administrative and economic reasons. In this research we propose an inference to drug effects when subjects are dropped out in the planed-for 3$\times$3 and 3$\times$2 crossover experiments. An example is given for illustration.

• Communications for Statistical Applications and Methods
• /
• v.9 no.3
• /
• pp.743-752
• /
• 2002

The estimation of the response curve is the important problem in the quantal bioassay. When we estimate the response curve, we determine the design points in advance of the experiment. Then naturally we have a question of which design would be optimal. As a response curve estimator, locally weighted quasi-likelihood estimator has several more appealing features than the traditional nonparametric estimators. The optimal design density for the locally weighted quasi-likelihood estimator is derived and its ability both in theoretical and in empirical point of view are investigated.

• Journal of Korean Society for Quality Management
• /
• v.23 no.3
• /
• pp.102-112
• /
• 1995

Estimation of each of mean response, difference between mean responses and derivatives of the response function is a possible objective of a response surface design. These objectives are to be achieved simultaneously when an experiment is designed to improve mean response. For the situations where departure from the assumed model is suspected, first and second order designs for improving mean response are obtained by combining minimum bias designs for the individual design objectives. D- and A-optimalities are used for selecting specific second order designs. The results are applied to central composite designs.

• Journal of the Korean Data and Information Science Society
• /
• v.18 no.3
• /
• pp.745-755
• /
• 2007

The concept of slope rotatability introduced by Hader and Park(1978) is available when we are interested in the difference of the responses. Since there can be constraints on the factor levels in mixture experiments, there arises a need for adaptation of the concept of slope rotatability and the measure to assess it. In this article, measures of slope rotatability in mixture experiments are proposed to quantify the amount of slope rotatability for a given design. Measures for a restricted region design as well as for an unrestricted region design are presented. Then, the designs having different optimalities are compared with respect to these measures by some examples.