Adaptive confidence bands in the nonparametric fixed design regression model

In this note, we consider the problem of the existence of adaptive confidence bands in the fixed design regression model, adapting ideas in Hoffmann and Nickl [(2011), ‘On Adaptive Inference and Confidence Bands’, Annals of Statistics, 39, 2383–2409] to the present case. In the course of the proof, we show that sup-norm adaptive estimators exist as well in the regression setting.     

A comparison of test statistics for the recovery of rapid growth‐based enumeration tests

This paper considers five test statistics for comparing the recovery of a rapid growth‐based enumeration test with respect to the compendial microbiological method using a specific nonserial dilution experiment. The finite sample distributions of these test statistics are unknown, because they are functions of correlated count data. A simulation study is conducted to investigate the type I and type II error rates. For a balanced experimental design, the likelihood ratio test and the main effects analysis of variance (ANOVA) test for microbiological methods demonstrated nominal values for the type I error rate and provided the highest power compared with a test on weighted averages and two other ANOVA tests. The likelihood ratio test is preferred because it can also be used for unbalanced designs. It is demonstrated that an increase in power can only be achieved by an increase in the spiked number of organisms used in the experiment. The power is surprisingly not affected by the number of dilutions or the number of test samples. A real case study is provided to illustrate the theory. Copyright © 2013 John Wiley & Sons, Ltd.     

Development of an integrated information management model: A case of textile industry

For past two decades many organisations have tried to implement integrated information management systems for better production management based on co-ordination of information and therefore activities of different departments. Although Enterprise Resource Planning systems have been in the market for the past decade, many industries find it difficult to implement such systems due to the amount of work involved in streamlining the documentation, customisation and of high costs reported in implementation. An attempt is made through this paper to propose an integrated model, which can be easily understood by production personnel, with specific emphasis on the textile sector.     

A simplified proof of a theorem on the difference of the moore–penrose inverses of two positive semi–difinte matrices

Simplified proofs are given of a standard result that establishes positive semi–definiteness of the difference of the inverses of two non–singular matrices, and of the extension of this result by Milliken and Akdeniz (1977) to the difference of the Moore–Penrose inverse of two singular matrices.     

A nonparametric bayesian approach to analysis of drug dependence data

In this paper, a nonparametric Bayesian approach to the analysis of binary response data is considered. Using a Dirichlet process prior, and squared error loss, the Bayes estimators of response probabilities are obtained. Finally, the results obtained are employed to analyze the ARC 090 Trial data.     

Non-linear regression models for Approximate Bayesian Computation

Approximate Bayesian inference on the basis of summary statistics is well-suited to complex problems for which the likelihood is either mathematically or computationally intractable. However the methods that use rejection suffer from the curse of dimensionality when the number of summary statistics is increased. Here we propose a machine-learning approach to the estimation of the posterior density by introducing two innovations. The new method fits a nonlinear conditional heteroscedastic regression of the parameter on the summary statistics, and then adaptively improves estimation using importance sampling. The new algorithm is compared to the state-of-the-art approximate Bayesian methods, and achieves considerable reduction of the computational burden in two examples of inference in statistical genetics and in a queueing model.     

Caregiver Emotional Expressiveness,Child Emotion Regulation,and Child Behavior Problems among Head Start Families

The present study examined the relationships between caregivers' self‐reported positive and negative emotional expressiveness, observer assessments of children's emotion regulation, and teachers' reports of children's internalizing and externalizing behaviors in a sample of 97 primarily African American and Hispanic Head Start families. Results indicated that higher caregiver negativity and lower child emotion regulation independently predicted more internalizing behavior problems in children. Additionally, children's externalizing behavior problems were negatively predicted by caregivers' self‐reports of positive emotional expressiveness. Importantly, results also suggested that caregivers' emotional expressiveness and children's behavioral problems may be non‐linearly related, and that child gender may play an important moderating role. These results emphasize the importance of family emotional climate and child emotion regulation in the behavioral development of preschool‐age children, and highlight the need for improved theoretical and practical understanding of socioemotional development in diverse populations.