A-optimal diallel crosses for test versus control comparisons

A-optimality of block designs for control versus test comparisons in diallel crosses is investigated. A sufficient condition for designs to be A-optimal is derived. Type S₀ designs are defined and A-optimal type S₀ designs are characterized. A lower bound to the A-efficiency of type S₀ designs is also given. Using the lower bound to A-efficiency, type S₀ designs are shown to yield efficient designs for test versus control comparisons.     

Standardization of Process Norms in Baker's Yeast Fermentation through Statistical Models in Comparison with Neural Networks

Achieving consistency of growth pattern for commercial yeast fermentation over batches through addition of water, molasses and other chemicals is often very complex in nature due to its bio-chemical reactions in operation. Regression models in statistical methods play a very important role in modeling the underlying mechanism, provided it is known. On the contrary, artificial neural networks provide a wide class of general-purpose, flexible non-linear architectures to explain any complex industrial processes. In this paper, an attempt has been made to find a robust control system for a time varying yeast fermentation process through statistical means, and in comparison to non-parametric neural network techniques. The data used in this context are obtained from an industry producing baker's yeast through a fed-batch fermentation process. The model accuracy for predicting the growth pattern of commercial yeast, when compared among the various techniques used, reveals the best performance capability with the backpropagation neural network. The statistical model used through projection pursuit regression also shows higher prediction accuracy. The models, thus developed, would also help to find an optimum combination of parameters for minimizing the variability of yeast production.     

The Melancholic Name

How to think the gift of the name that announces the finitude of the named and the name itself, insofar as finitude is not reducible to the negativity of the concept and sense, a Gift to the other insofar as the other concerns us with his finitude? There is, as it were, a note of melancholy that adheres our finite existence, which also intimates the very gift that is given to the other. The whole question of the ethico‐political, understood here as being‐towards‐others, is at stake here. The article reads Schelling, Heidegger and Benjamin to think anew the place of language as gift and its concern with the thoughts of the political.     

Selection of a stroke risk model based on transcranial Doppler ultrasound velocity

Increased transcranial Doppler ultrasound (TCD) velocity is an indicator of cerebral infarction in children with sickle cell disease (SCD). In this article, the parallel genetic algorithm (PGA) is used to select a stroke risk model with TCD velocity as the response variable. Development of such a stroke risk model leads to the identification of children with SCD who are at a higher risk of stroke and their treatment in the early stages. Using blood velocity data from SCD patients, it is shown that the PGA is an easy-to-use computationally variable selection tool. The results of the PGA are also compared with those obtained from the stochastic search variable selection method, the Dantzig selector and conventional techniques such as stepwise selection and best subset selection.     

Corrected likelihood-ratio tests in logistic regression using small-sample data

Likelihood-ratio tests (LRTs) are often used for inferences on one or more logistic regression coefficients. Conventionally, for given parameters of interest, the nuisance parameters of the likelihood function are replaced by their maximum likelihood estimates. The new function created is called the profile likelihood function, and is used for inference from LRT. In small samples, LRT based on the profile likelihood does not follow χ² distribution. Several corrections have been proposed to improve LRT when used with small-sample data. Additionally, complete or quasi-complete separation is a common geometric feature for small-sample binary data. In this article, for small-sample binary data, we have derived explicitly the correction factors of LRT for models with and without separation, and proposed an algorithm to construct confidence intervals. We have investigated the performances of different LRT corrections, and the corresponding confidence intervals through simulations. Based on the simulation results, we propose an empirical rule of thumb on the use of these methods. Our simulation findings are also supported by real-world data.     

Binary designs are not always the best

We give an example of a nonbinary block design which is better than any binary design with respect to the E-optimality criterion. This shows that the class of binary designs is not essentially complete, at least with respect to E-optimality.