“When will They Ever Learn”? Selective Discrimination in Provision of Schooling Facilities in Muslim Majority Areas: A Case Study of Murshidabad District of West Bengal,India

Inequality in educational attainment among various religious communities in India has come to light in the public domain only after the publication of Sachar Committee Report. The committee opined that selective discrimination of schooling facilities in Muslim concentrated areas is one of the major reasons behind the low educational attainment of that community. The present study aims at understanding the disparities in the provision of schooling facilities in Muslim-dominated and non-Muslim-dominated regions of West Bengal considering Murshidabad district as the study area. The present study constructed an Educational Development Index by considering 11 socioeconomic indicators and using the principal component analysis method, applied it to the study population. It has been found that the blocks having a higher share of Muslim population registered lower levels of educational development. It has also been found that most of the deprived schools located in the Muslim-dominated areas and within the non-Muslim-dominated areas the schools having higher share of Muslim pupil are mostly deprived.     

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.     

Two Non-Negative Estimators for the Model With a Common Mean

Recently C. R. Rao (1984) suggested two modifications to the MINQUE, called MINQUE(S.D.) and MINQUE(C.P.), for estimating the variance components in a linear model. These modifications provide non-negative estimates unlike the MINQUE. In this article we shall compare the performance of these two estimators with some of the other existing modifications of MINQUE in terms of standard criteria such as the mean squared error and total squared bias.     

Bias correction in logistic regression with missing categorical covariates

Logistic regression plays an important role in many fields. In practice, we often encounter missing covariates in different applied sectors, particularly in biomedical sciences. Ibrahim (1990) proposed a method to handle missing covariates in generalized linear model (GLM) setup. It is well known that logistic regression estimates using small or medium sized missing data are biased. Considering the missing data that are missing at random, in this paper we have reduced the bias by two methods; first we have derived a closed form bias expression using Cox and Snell (1968), and second we have used likelihood based modification similar to Firth (1993). Here we have analytically shown that the Firth type likelihood modification in Ibrahim led to the second order bias reduction. The proposed methods are simple to apply on an existing method, need no analytical work, with the exception of a little change in the optimization function. We have carried out extensive simulation studies comparing the methods, and our simulation results are also supported by a real world data.     

On D-optimal robust designs for lifetime improvement experiments

Locating the optimal operating conditions of the process parameters is critical in a lifetime improvement experiment. For log-normal lifetime distribution with compound error structure (i.e., symmetry, inter-class and intra-class correlation error structures), we have developed methods for construction of D-optimal robust first order designs. It is shown that D-optimal robust first order designs are always robust first order rotatable but the converse is not always true.     

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.