A perspective of composite sampling

Composite samples are formed by physically mixing samples. Usually, composite samples are used to reduce the overall cost associated with analytical procedures that must be performed on each sample, but they can also be used to protect the privacy of individuals.

Composite sampling can reduce the cost of identifying individual cases that have a certain trait, such as those with a rare disease or those exceeding pollution-level standards. Not much is lost by applying this method as long as the trait is relatively rare.

Composite sampling can reduce the cost of estimating the mean of some process. When samples are composited, the ability to estimate the variance is lost. In spite of this, the potential savings are so great that composite samples have been used.

Much of this paper deasl with the variance of estimators based on composite sampling when the porportions of hte original samples comprising the composite sample are actually random. Taking repeated samples and measurements on several composite samples complicates the prodcedure, but allows the estimation of between and within variation as well as measurement error.     

Testing parameter of the power series distribution of a zero inflated power series model

The Zero Inflated Power Series Distribution (ZIPSD) contains two parameters. The first parameter indicates inflation of zero and the other parameter is that of the Power Series distribution. We provide three asymptotic tests for testing the parameter of Power Series distribution, using an unconditional (standard) likelihood approach, a conditional likelihood approach and a test based on sample mean, respectively. The performance of these three tests has been studied for Zero Inflated Poisson Distribution (ZIPD). Asymptotic Confidence Intervals for the parameter are also provided.     

On additive and multiplicative damage models and the characterizations of linear and logarithmic exponential families

Rao (1963) has formulated a damage model which we call an additive damage model. A suitable damage model, which we call a multiplicative damage model, has been considered by Krishnaji (1970) for income-related problems. In these models, an original observation is subjected to damage, e.g., death or under-reporting, according to a specified probability law. Within the framework of an additive damage model, with a special form of damage, characterizations of the linear and logarithmic exponential families are formulated using regression properties of the damaged part on the undamaged part. The characterizations of the gamma and Pareto distributions that have been found of some use in the theory of income distributions, are obtained as special cases. Similar results are investigated within the framework of the multiplicative damage model.     

Variable Bandwidths for Nonparametric Hazard Rate Estimation

A smoothing parameter inversely proportional to the square root of the true density is known to produce kernel estimates of the density having faster bias rate of convergence. We show that in the case of kernel-based nonparametric hazard rate estimation, a smoothing parameter inversely proportional to the square root of the true hazard rate leads to a mean square error rate of order n ^?8/9, an improvement over the standard second order kernel. An adaptive version of such a procedure is considered and analyzed.     

Tests for Equality of Inflation Parameters of Two Zero-Inflated Power Series Distributions

In this article, we consider two independent zero-inflated power series distributions and provide likelihood ratio test for equality of inflation parameters of the same. As an illustration, testing equality of inflation parameters of two zero inflated Poisson distributions is provided. Further, simulation study to investigate power of likelihood ratio tests has been carried out.     

Invariance of linearity of regression and related characterizations of some classical discrete distributions

Rao (1963) introduced what we call an additive damage model. In this model, original observation is subjected to damage according to a specified probability law by the survival distribution. In this paper, we consider a bivariate observation with second component subjected to damage. Using the invariance of linearity of regression of the first component on the second under the transition of the second component from the original to the damaged state, we obtain the characterizations of the Poisson, binomial and negative binomial distributions within the framework of the additive damage model.     

Fishery and Forestry Management

There are a large number of different definitions used for sample quantiles in statistical computer packages. Often within the same package one definition will be used to compute a quantile explicitly, while other definitions may be used when producing a boxplot, a probability plot, or a QQ plot. We compare the most commonly implemented sample quantile definitions by writing them in a common notation and investigating their motivation and some of their properties. We argue that there is a need to adopt a standard definition for sample quantiles so that the same answers are produced by different packages and within each package. We conclude by recommending that the median-unbiased estimator be used because it has most of the desirable properties of a quantile estimator and can be defined independently of the underlying distribution.     

Unhealthy Weight in Indian Families: The Role of the Family Environment in the Context of the Nutrition Transition

India faces a dual burden of increasing obesity and persistent underweight as it experiences the nutrition transition—the dietary and lifestyle changes that accompany globalization, economic development, and technological change. Yet, the nutrition transition is not solely a top-down process; rather, global forces converge with local practices at multiple levels of the social ecology. The family environment, a key site for the transmission of local customs and norms, remains largely unexplored in India. We examined the extent to which opposite-gender siblings and mother–child pairs were concordant or discordant in body weight, and whether domains of the family environment, specifically, food practices, food-related gender norms, and household resources, were associated with patterns of unhealthy weight within and between families. Multilevel dyadic analysis and logistic regression were conducted using survey data from a representative sample of 400 families in a Southern Indian city. We identified substantial clustering of weight among opposite-gender sibling pairs (ICC = 0.43) and mother–child pairs, as well as important patterns of discordance, including 11% of families experiencing a dual burden of underweight and overweight. Household resources, including mother’s education and income, were salient in explaining the distribution of body weight within and between families. Importantly, less examined domains of the family environment were also relevant, including food practices (e.g., grocery shopping frequency), and food-related gender norms (e.g., mother’s control of food served at home). Continued exploration of how global and local practices converge in households will be necessary to develop programming that effectively addresses India’s dual burden of unhealthy weight.     

Transnational Feminism in Sociology: Articulations,Agendas, Debates

What is the current terrain of transnational feminism in sociology? This essay begins by introducing the key interdisciplinary writings that helped initiate a specifically transnational feminist approach in women’s and gender studies. It then delineates three primary ways in which these writings have been adapted and/or expanded with/in sociology, as well as some points of tension and debate among these varying approaches. It argues that although there are numerous sociologically oriented transnational feminisms, most authors do not acknowledge this multiplicity and complexity, contributing to a sometimes confusing conversation where assumptions about key concepts, causality, and processes remain unclear. The essay ends with suggestions for future research.     

Identification and Review of Sensitivity Analysis Methods

Identification and qualitative comparison of sensitivity analysis methods that have been used across various disciplines, and that merit consideration for application to food-safety risk assessment models, are presented in this article. Sensitivity analysis can help in identifying critical control points, prioritizing additional data collection or research, and verifying and validating a model. Ten sensitivity analysis methods, including four mathematical methods, five statistical methods, and one graphical method, are identified. The selected methods are compared on the basis of their applicability to different types of models, computational issues such as initial data requirement and complexity of their application, representation of the sensitivity, and the specific uses of these methods. Applications of these methods are illustrated with examples from various fields. No one method is clearly best for food-safety risk models. In general, use of two or more methods, preferably with dissimilar theoretical foundations, may be needed to increase confidence in the ranking of key inputs.