The nearest shrunken centroid classifier uses shrunken centroids as prototypes for each class and test samples are classified to belong to the class whose shrunken centroid is nearest to it. In our study, the nearest shrunken centroid classifier was used simply to select important genes prior to classification. Random Forest, a decision tree based classification algorithm, is chosen as a classi...

the empirical bayes estimators of treatment effects in a factorial experiment were derived and their asymptotic properties were explored. it was shown that they were asymptotically optimal and the estimator of the scale parameter had a limiting gamma distribution while the estimators of the factor effects had a limiting multivariate normal distribution. a bootstrap analysis was performed to ill...

We propose a wavelet based regression function estimator for the estimation of the regression function for a sequence of ?-missing random variables with a common one-dimensional probability density function. Some asymptotic properties of the proposed estimator based on block thresholding are investigated. It is found that the estimators achieve optimal minimax convergence rates over large class...

this paper proposes a discrete-time repetitive optimal control of electrically driven robotic manipulators using an uncertainty estimator. the proposed control method can be used for performing repetitive motion, which covers many industrial applications of robotic manipulators. this kind of control law is in the class of torque-based control in which the joint torques are generated by permanen...

the statistical analysis of spatial data is usually done under gaussian assumption for the underlying random field model. when this assumption is not satisfied, block bootstrap methods can be used to analyze spatial data. one of the crucial problems in this setting is specifying the block sizes. in this paper, we present asymptotic optimal block size for separate block bootstrap to estimate the...