Abstract: An attempt has been made in the present
communication to elucidate the efficacy of robust ANOVA methods
to analyse horticultural field experimental data in the presence of
outliers. Results obtained fortify the use of robust ANOVA methods
as there was substantiate reduction in error mean square, and hence
the probability of committing Type I error, as compared to the regular
approach.
Abstract: The objective of meta-analysis is to combine results
from several independent studies in order to create generalization
and provide evidence base for decision making. But recent studies
show that the magnitude of effect size estimates reported in many
areas of research significantly changed over time and this can
impair the results and conclusions of meta-analysis. A number of
sequential methods have been proposed for monitoring the effect
size estimates in meta-analysis. However they are based on statistical
theory applicable only to fixed effect model (FEM) of meta-analysis.
For random-effects model (REM), the analysis incorporates the
heterogeneity variance, τ 2 and its estimation create complications.
In this paper we study the use of a truncated CUSUM-type test with
asymptotically valid critical values for sequential monitoring in REM.
Simulation results show that the test does not control the Type I error
well, and is not recommended. Further work required to derive an
appropriate test in this important area of applications.
Abstract: The unit root tests based on the robust estimator for the first-order autoregressive process are proposed and compared with the unit root tests based on the ordinary least squares (OLS) estimator. The percentiles of the null distributions of the unit root test are also reported. The empirical probabilities of Type I error and powers of the unit root tests are estimated via Monte Carlo simulation. Simulation results show that all unit root tests can control the probability of Type I error for all situations. The empirical power of the unit root tests based on the robust estimator are higher than the unit root tests based on the OLS estimator.
Abstract: Traditional multivariate control charts assume that measurement from manufacturing processes follows a multivariate normal distribution. However, this assumption may not hold or may be difficult to verify because not all the measurement from manufacturing processes are normal distributed in practice. This study develops a new multivariate control chart for monitoring the processes with non-normal data. We propose a mechanism based on integrating the one-class classification method and the adaptive technique. The adaptive technique is used to improve the sensitivity to small shift on one-class classification in statistical process control. In addition, this design provides an easy way to allocate the value of type I error so it is easier to be implemented. Finally, the simulation study and the real data from industry are used to demonstrate the effectiveness of the propose control charts.
Abstract: In this paper, the concepts of dichotomous logistic
regression (DLR) with leave-one-out (L-O-O) were discussed. To
illustrate this, the L-O-O was run to determine the importance of the
simulation conditions for robust test of spread procedures with good
Type I error rates. The resultant model was then evaluated. The
discussions included 1) assessment of the accuracy of the model, and
2) parameter estimates. These were presented and illustrated by
modeling the relationship between the dichotomous dependent
variable (Type I error rates) with a set of independent variables (the
simulation conditions). The base SAS software containing PROC
LOGISTIC and DATA step functions can be making used to do the
DLR analysis.
Abstract: It is a one-sided hypothesis testing process for assessing bioequivalence. Bootstrap and modified large-sample(MLS) methods are considered to study individual bioequivalence(IBE), type I error and power of hypothesis tests are simulated and compared with FDA(2001). The results show that modified large-sample method is equivalent to the method of FDA(2001) .