Application of Multivariate Analysis to Increase the Yield of Dry Cell Batteries

In an organization, the manufacturing process of a dry cell battery was suffering from the problem of frequent stoppages in the assembly line. The complete battery manufacturing operation is highly automated and mechanized. It was suspected that excessive variation in overall height of bobbin was the major reason for such stoppages. The bobbin, the inside part of a dry cell battery acting as cathode, is formed by the battery extrusion process. A planned experiment was carried out on the extrusion process that identified the setting of extrusion machines and the amount of water content in the cathode mixture as the parameters causing variation in the bobbin characteristics. The problem of frequent stoppages was eliminated when appropriate action was taken on these two parameters. Finally, multivariate and univariate control schemes were developed for online control of the bobbin characteristics.     

Union–intersection principle and constrained statistical inference

Most statistical models arising in real life applications as well as in interdisciplinary research are complex in their designs, sampling plans, and associated probability laws, which in turn are often constrained by inequality, order, functional, shape or other restraints. Optimality of conventional likelihood ratio based statistical inference may not be tenable here, although the use of restricted or quasi-likelihood has spurred in such environments. S.N. Roy's ingenious union–intersection principle provides an alternative avenue, often having some computational advantages, increased scope of adaptability, and flexibility beyond conventional likelihood paradigms. This scenario is appraised here with some illustrative examples, and with some interesting problems of inference on stochastic ordering (dominance) in parametric as well as beyond parametric setups.     

Regression models for analyzing clustered binary and continuous outcomes under an assumption of exchangeability

Scientific experiments commonly result in clustered discrete and continuous data. Existing methods for analyzing such data include the use of quasi-likelihood procedures and generalized estimating equations to estimate marginal mean response parameters. In applications to areas such as developmental toxicity studies, where discrete and continuous measurements are recorded on each fetus, or clinical ophthalmologic trials, where different types of observations are made on each eye, the assumption that data within cluster are exchangeable is often very reasonable. We use this assumption to formulate fully parametric regression models for clusters of bivariate data with binary and continuous components. The regression models proposed have marginal interpretations and reproducible model structures. Tractable expressions for likelihood equations are derived and iterative schemes are given for computing efficient estimates (MLEs) of the marginal mean, correlations, variances and higher moments. We demonstrate the use the ‘exchangeable’ procedure with an application to a developmental toxicity study involving fetal weight and malformation data.     

Some remarks on classical and bayesian reliability estimation of binomial and poisson distributions

The problems of estimating the reliability function and Pr{X₁+...+X_k ≤ Y} are considered. The random variables X’s and Y are assumed to follow binomial and Poisson distributions. Classical estimators available in the literature are discussed and Bayes estimators are derived. In order to obtain the estimators of these parametric functions, the basic role is played by the estimators of factorial moments of the two distributions.     

Response to the COVID-19: Understanding implications of government lockdown policies

The rising number of COVID-19 cases and economic implications of lockdown measures indicate the tricky balancing act policy makers face as they implement the subsequent phases of 'unlock'. We develop a model to examine how lockdown and social distancing measures have influenced the behavioral conduct of people. The current situation highlights that policy makers need to focus on bringing awareness and social restraint among people rather than going for stringent lockdown measures. We believe this work will help the policy makers gain insights into the troubled COVID-19 times ahead, and based on the estimates, they can frame policies to navigate these wild waves in the best possible way.     

On a class of variance balanced incomplete block designs

In this paper variance balanced incomplete block designs have been constructed for situations when suitable BIB designs do not exist for a given number of treatments, because of the contraints bk=vr, λ(v-1) = r(k-l). These variance balanced designs are in unequal block sizes and unequal replications.     

Small domain estimation by empirical bayes and kalman filtering procedures - a case study

An application of empirical Bayes and Kalman filtering tecniques is reported, using live data from Indian Statistical Institute (ISI), Calcutta . to illustrate how initial small domain estimators may be vastly improved upon. A stratified two stage sampling procedure is adopted, allowing selection of first stage units with unequal probabilities but of second stage units with equal probabilities. Standard design-based estimators for domain totals are initialized based on domain specific survey data alone. Strength is then borrowed across domains and from past surveys. The resulting gains in efficacy are numlerically demonstrated, through replicated sampling from official records.     

The Blended Family Life Cycle

Blended families face unique challenges that differ greatly from those encountered by nuclear families. Societal stereotypes that view blended families as abnormal, taking nuclear family functioning as the prototype for all family units, create a lack of role clarity for each of the members involved. Although many strategies exist with which to help families, many are based on nuclear family systems and thus are inapplicable to blended families. The creation of a blended family life cycle specific to blended family systems could help members of a blended family create a successful family unit as they work with a counselor.     

A Lean transportation approach for improving emergency medical operations

Abstract

This paper extends the approach of Lean transportation to improve the performance of emergency medical processes (EMP) by adapting its main concepts into the main characteristics of the EMP environment. The paper is based on an inductive theory-building process initiated from a case study in the field of emergency medical services. The development of the suggested performance improvement approach triggered from an exhaustive analysis of a process involved and an exploratory review of the existing improvement approaches available. The process of concern can be described as a specialized transportation process, where human lives are at risk. Its characteristics led to a natural application of Lean transportation. The results include, a novel approach for improving the agility and efficient EMS processes. This is validated with an application for improving ambulance response times and turnaround times of the Red Cross operations located in Monterrey, Mexico. The approach proved to be an integrated scheme for identifying waste opportunities at a systems level. This characteristic is important to let operations management prioritize improvement efforts in a limited budget situation. This work suggests the application of the emerging Lean transportation approach for increasing the agility performance of EMS processes.     

Nonparametric repeated significance tests for some analysis of covariance models

For a general class of nonparametric analysis of covariance problems (with stochastic covariates), some repeated significance testing procedures are developed. These procedures rest on the construction of suitable rank order statistics based on the partial sequence of sample sizes and allow for a monitoring of experimentation with the objective of a possible early termination of experimentation. The basic theory is based on the weak convergence of certain stochastic processes relating to the rank order statistics. Various properties of the proposed tests are discussed.