Kamon Budsaba

COMPASS PLOTS: A COMBINATION OF STAR PLOT AND ANALYSIS OF MEANS TO VISUALIZE SIGNIFICANT INTERACTIONS IN COMPLEX TOXICOLOGY STUDIES

The Compass Plot, a new graphical methodthat combines the advantages of the star plot andthe analysis of means (ANOM), has beenintroducedfordisplaying the results of experiments. Incontrast to the star plot, this plot allows statistical inferences to be made for treatment and for factor effects. In contrast to ANOM, this plot is good for comparison in multiresponse experiments, a scenario becoming more commonplace in toxicology with the advent of increasing numbers of chemical mixture studies. An example of a 2 3 factorial chemical exposure experiment is considered here.

Strong Laws for Certain Types of U-statistics Based on Negatively Associated Random Variables

We establish the Marcinkiewicz-Zygmund-type strong laws of large numbers for certain class of multilinear U-statistics based on negatively associated random variables.

Using a Genetic Algorithm to Generate D s -Optimal Designs for Mixture Experiments in a Simplex Region

We propose and develop a genetic algorithm(GA) to generate Ds-optimal or near-optimal mixture designs of a simplex region that maximizes the Ds-criterion while simultaneously guaranteeing an acceptable D-efficiency for the mixture experiments. Our method does not need a candidate set which makes it possible to select points throughout a continuous region. Two new GAs are developed to handle specific subsets of quadratic and special cubic mixture model terms. Summaries of GA designs are reported for 3 and 4 mixture components. The performance of GA designs is assessed in comparisons with the designs generated from an exchange algorithm (EA) and SAS Proc OPTEX computer generated designs (CGDs). The results show that the Ds-criterion values of the GA designs for all percentage of D-efficiency were greater than or equal to those of the EA and SAS Proc OPTEX designs(CGDs). This suggests that the GA is a effective method for generating subset optimal designs for mixture experiments.

The effects of violations of the multivariate normality assumption in multivariate Shewhart and MEWMA control charts

A multivariate Shewhart and a multivariate exponentially weighted moving average control charts are types of multivariate control charts for monitoring the mean vector. For those control charts, a multivariate normal distribution is an important assumption that is used to describe a behavior of a set of quality characteristics of interest. This research explores the sensitivity of average run lengths and standard deviation of run lengths for the multivariate Shewhart and the multivariate exponentially weighted moving average control charts when the normality assumption is incorrect.

The effects of violations of assumptions in multivariate Shewhart control charts

In manufacturing processes, a vector of multiple responses is often monitored to assess if the process is in-control. A multivariate Shewhart control chart is one method of monitoring the mean vector. If the chi-square statistic exceeds an upper control limit (UCL), then an out-of-control signal occurs. The average run length (ARL) is used to determine the UCL value. ARLs have been estimated under an assumption of a multivariate normal (MVN) assumption. This research explores the sensitivity of ARLs when the MVN assumption is incorrect. ARLs for data from Multivariate t, lognormal, uniform, and beta distributions are estimated and compared to ARLs under the MVN assumption.

Statistical Analysis on Satisfaction of Residents Living in Prefabricated Concrete Buildings in Thailand

Sample survey has been conducted to evaluate satisfaction of residents living in prefabricated concrete buildings (dormitories, detached houses, and townhouses) in Thailand. Nine criteria have been determined including moisture protection, noise prevention, safety of structures, thermal prevention, air flow, external appearance, interior, facility, and overall satisfaction. The t-test indicates that the satisfaction score mean between males and females for each criterion is not significantly different. The Levenes test shows that our survey data do not follow homogeneity of variance assumption for ANOVA F-test for most criteria, except for airflows and overall satisfaction criteria. Welch and Brown-Forsythe tests are then used under non-homogeneity of variances. The tests reveal that all criteria show a significant difference among habitats groups, but not for the thermal prevention criterion. Some multiple comparisons also show many pairs of significant mean difference among habitat groups.