Small area estimation has received enormous attention in recent years due to its wide range of application, particularly in policy making decisions. The variance based on direct sample size of small area estimator is unduly large and there is a need of constructing model based estimator with low mean squared prediction error (MSPE). Estimation of MSPE and in particular the bias correction of MS...

Being able to predict the final product yield at all stages in long-running, industrial, mammalian cell culture processes is vital for both operational efficiency, process consistency, and the implementation of quality by design (QbD) practices. Here we used Raman spectroscopy to monitor (in terms of glycoprotein yield prediction) a fed-batch fermentation from start to finish. Raman data were c...

This paper proposes a novel high capacity reversible image data hiding scheme using a prediction technique which is effective for error resilience in H.264/AVC. In the proposed method, which is based on H.264/AVC intra prediction, firstly the prediction error blocks are computed and then the error values are slightly modified through shifting the prediction errors. The modified errors are used ...

Overall error rate (all four criteria combined) for the nonintervention group decreased 4.9% (p> 0.05). Overall error rates for the intervention group and the total study, however, significantly decreased 9.8% and 7.8%, respectively. Error rate for over-prescribing increased 3.1 % in the total study and 9.4% in the nonintervention group. Error rate for over-prescribing in the intervention group...

Hierarchical models are utilized in a wide variety of problems which are characterized by task hierarchies, where predictions on smaller subtasks are useful for trying to predict a final task. Typically, neural networks are first trained for the subtasks, and the predictions of these networks are subsequently used as additional features when training a model and doing inference for a final task...

This paper provides error analysis for distributed semi-supervised learning with kernel ridge regression (DSKRR) based on a divide-and-conquer strategy. DSKRR applies kernel ridge regression (KRR) to data subsets that are distributively stored on multiple servers to produce individual output functions, and then takes a weighted average of the individual output functions as a final estimator. Us...

this paper proposes a new approach for assessing the efficiency of barriers and of barrier removals. Facing defences in depth organized by physical barriers implemented on the human-machine system or by immaterial barriers such as procedures to be applied, barrier removal is an intentional human error and consists in not respecting the existing barriers of the human-machine system. An approach ...