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Item Robust D-Optimal Design for Multiple Nominal Parameter Values under the 5PL-1P Model(North Dakota State University, 2018) Liang, CuipingA robust D-optimal design that works well for multiple nominal parameter values is presented in this paper. In general, D-optimal design works very well for estimating the model parameters, but it is very sensitive to multiple nominal model parameter values when the response is modeled by nonlinear models. The 5PL-1P model is considered in this study to describe a dose-response function. The sensitivity of the D-optimal design to the model parameter values under the 5PL-1P model is studied. The robust D-optimal design that can reduce the impact of the multiple nominal model parameter values is proposed using the Bayesian technique. Lastly, we compare performances of the proposed design to other well-known designs for estimating the model parameters under the 5PL-1P model.Item Comparing Tests for a Mixed Design with Block Effect(North Dakota State University, 2009) Zhao, HuiTests Comb and Comb II are used to test the equality of means in a mixed design which is a combination of randomized complete block design and completely randomized design. The powers of Comb and Comb II for a mixed design have already been compared with Page's test (Magel, Terpstra, Wen (2009)) when there was little or no block effect added to the portion that was analyzed as a completely randomized design. In this paper, we wish to compare the tests when the portion of the design analyzed as a completely randomized design actually has a block effect. A Monte Carlo simulation study was conducted to compare the power of the three tests where Page's test was used only on data from the randomized complete block portion. A variety of situations were considered. Three underlying distributions were included in the simulation study. These included the normal distribution, exponential distribution, and t distribution with degree of freedom equal to 3. For every distribution, 16, 32 and 40 blocks were used in the randomized complete block design portion where the equal sample size of completely randomized data portion was 1/8, 1/4 and 1/2 the number of blocks considered. Unequal sample sizes for the completely randomized design portion were also considered. Powers were estimated for different location parameter arrangements for 3, 4 and 5 populations. Two variances, 0.25 and I, for the block effect were used. The block factor added into the completely randomized design portion didn't change the test with highest rejection percentage for the equal sample size cases, although the powers of the two tests for the mixed design decreased. For most of unequal sample size cases, Page's test has the highest rejection percentage. Overall, it was concluded that it was better to use one of the two tests for mixed design instead of Page's test when there were equal sample sizes for portion analyzed as a completely randomized design. When there were not equal size samples, but the first sample size was twice the size of the others, it was generally better to use Comb over Page's unless the number of populations became very large or there was a large block effect variance.Item Robust D-Optimal Design for Response Functions with a Downturn(North Dakota State University, 2013) Carter, Jessica AnneResearchers studying dose-response relationships must allocate limited resources to design points in order to maximize the information gained from the study. D-optimal design is a well-described design that works efficiently to study model parameters. In order to find the D-optimal design, the model that describes the dose-response relationship has to be known. In cases where dose-response relationships show a downturn at high doses, scientists sometimes ignore the downturn to study only the increasing part of the response curve. Here we have two model choices; one describes the overall dose-response relationship, and the other describes only the increasing part of the response curve. The D-optimal designs for these two models will be different and the D-optimal design for one model may not work efficiently for the other model. This research studies robust D-optimal design, a design that works efficiently for both models.