Modelling process robustness: a case study of centrifugal casting

A robust process minimises the effect of the noise factors on the performance of a product or process. The variation of the performance of a robust process can be measured through modelling and analysis of process robustness. In this paper, a comprehensive methodology for modelling and analysis of process robustness is developed considering a number of relevant tools and techniques such as multivariate regression, control charting and simulation within the broad framework of Taguchi method. The methodology as developed considers, in specific terms, process modelling using historical data pertaining to responses, inputs variables and parameters as well as simulated noise variables data, identification of the model responses at each experimental setting of the controllable variables, estimation of multivariate process capability indices and control of their variability using control charting for determining optimal settings of the process variables using design of experiment-based Taguchi Method. The methodology is applied to a centrifugal casting process that produces worm-wheels for steam power plants in view of its critical importance of maintaining consistent performance in various under controllable situations (input conditions). The results show that the process settings as determined ensure minimum in-control variability with maximum performance of the centrifugal casting process, indicating improved level of robustness.     

Improved Tolerance Factors for Multivariate Normal Distributions

The Q_os and Qm are two leading estimators of the probability of misclassification which are based on the asymptotic expansion of the the expected value of the Error Rate, P_i. The estimators are, however, not suitable for estimating the Error rates for certain ranges of the parameters p , n₁, n₂ and ß.We investigate the regions in which they produce unacceptable estimates , and show that the Q_os is, in general, better than the Q_m in producing acceptable estimates     

A new two sample type-II progressive censoring scheme

In this paper we introduce a new type-II progressive censoring scheme for two samples. It is observed that the proposed censoring scheme is analytically more tractable than the existing joint progressive type-II censoring scheme proposed by Rasouli and Balakrishnan. The maximum likelihood estimators of the unknown parameters are obtained and their exact distributions are derived. Based on the exact distributions of the maximum likelihood estimators exact confidence intervals are also constructed. For comparison purposes we have used bootstrap confidence intervals also. One data analysis has been performed for illustrative purposes. Finally we propose some open problems.     

Estimation of long-range dependence in gappy Gaussian time series

Statistics and Computing - Knowledge of the long-range dependence (LRD) parameter is critical to studies of self-similar behavior. However, statistical estimation of the LRD parameter becomes...     

Minimum-segment convex drawings of 3-connected cubic plane graphs

A convex drawing of a plane graph G is a plane drawing of G, where each vertex is drawn as a point, each edge is drawn as a straight line segment and each face is drawn as a convex polygon. A maximal segment is a drawing of a maximal set of edges that form a straight line segment. A minimum-segment convex drawing of G is a convex drawing of G where the number of maximal segments is the minimum among all possible convex drawings of G. In this paper, we present a linear-time algorithm to obtain a minimum-segment convex drawing Γ of a 3-connected cubic plane graph G of n vertices, where the drawing is not a grid drawing. We also give a linear-time algorithm to obtain a convex grid drawing of G on an $(\frac{n}{2}+1)\times(\frac {n}{2}+1)$ grid with at most s _n +1 maximal segments, where $s_{n}=\frac{n}{2}+3$ is the lower bound on the number of maximal segments in a convex drawing of G.     

Bird species assemblages across a rural urban gradient around Kolkata,India

Urban development leads to changes in habitat structure and resource base. Bird communities are known to respond sharply to such changes. Our result from study of bird community along urbanization gradient around Kolkata metropolitan city clearly separated the urban bird community from the rural and rural 3 habitats in terms of species diversity and foraging groups. Rural and rural 3 sites had more number of rare species and higher percentage of unique species as compared to the urban habitats. Functional group analysis showed higher abundance of granivores in urban habitats and absence of insectivore and carnivorous species that were found in the rural and rural 3 habitats. The bird species assemblage along the gradient was significantly nested where bird species recorded in urban areas were subset of the species rich rural areas. There was no difference in individual counts between urban and rural habitats, therefore nullifying the hypothesis that rural areas are more species rich because of higher population size. Bird community in the urban areas was less even as compared to the rural areas due to the dominance of omnivorous guild. Bird diversity was negatively correlated to the density of house lots.     

Improving early phase oncology clinical trial design: An opportunity for statisticians

This short communication supports that rule-based study designs such as the ‘3 + 3’ study design are still being used in early phase oncology development programs despite their inferior performance to model-based and model-assisted designs. Statisticians have an opportunity to shape and improve early phase oncology drug development programs by introducing newer, more efficient study designs that estimate the Optimal Biological dose to their oncology trialist colleges.