Economic statistical design of x ¥ control charts for systems with gamma ( 5 ,2) in-control times

In this paper, gamma ( 5 ,2) distribution is considered as a failure model for the economic statistical design of x ¥ control charts. The study shows that the statistical performance of control charts can be improved significantly, with only a slight increase in the cost, by adding constraints to the optimization problem. The use of an economic statistical design instead of an economic design results in control charts that may be less expensive to implement, that have lower false alarm rates, and that have a higher probability of detecting process shifts. Numerical examples are presented to support this proposition. The results of economic statistical design are compared with those of a pure economic design. The effects of adding constraints for statistical performance measures, such as Type I error rate and the power of the chart, are extensively investigated.     

An Optimal Multivariate Stratified Sampling Design Using Dynamic Programming

Numerous optimization problems arise in survey designs. The problem of obtaining an optimal (or near optimal) sampling design can be formulated and solved as a mathematical programming problem. In multivariate stratified sample surveys usually it is not possible to use the individual optimum allocations for sample sizes to various strata for one reason or another. In such situations some criterion is needed to work out an allocation which is optimum for all characteristics in some sense. Such an allocation may be called an optimum compromise allocation. This paper examines the problem of determining an optimum compromise allocation in multivariate stratified random sampling, when the population means of several characteristics are to be estimated. Formulating the problem of allocation as an all integer nonlinear programming problem, the paper develops a solution procedure using a dynamic programming technique. The compromise allocation discussed is optimal in the sense that it minimizes a weighted sum of the sampling variances of the estimates of the population means of various characteristics under study. A numerical example illustrates the solution procedure and shows how it compares with Cochran's average allocation and proportional allocation.     

Social Health Indicators and Economic Growth: Evidence from East Asia and Pacific,Middle East and North Africa,South Asia,and Sub-Saharan Africa

The objective of this study is to empirically investigate a two-way statistical relationship between the social health indicators and economic growth in the context of four major regions of the world i.e., East Asia and Pacific, Middle East and North Africa (MENA), South Asia and Sub-Saharan Africa. To recognize the relationship between the two variables, a time series, co-integration and Granger causality tests have been employed. Aggregate secondary data pertaining to these four regions from 1975 to 2011 on economic growth and social health indicators i.e., infant mortality, child abuse, child poverty, unemployment, weekly wages, health insurance coverage, teenage suicide, teenage drug abuse, high school dropouts, poverty, out-of-pocket health costs, homicides, alcohol related traffic fatalities, food insecurity, income inequality, HIV/AIDS, tuberculosis, basic health units and rural health centers has been used for analysis. This study evaluates four alternative but equally plausible hypotheses, each with different policy implications. These are: (1) social health indicators Granger cause economic growth, (2) economic growth Granger cause social health indicators (the conventional view), (3) There is a bi-directional causality between the two variables and (4) Both variables are causality independent (although highly correlated). The empirical results only moderately support the conventional view that economic growth has significant long run casual effect on social health indicators in East Asia and Pacific, MENA, South Asia and Sub-Saharan Africa. The present study find evident of unidirectional causality running towards economic growth to social health indicators, although, there are some bidirectional causality also exists between the variables. The percentage of unidirectional causality between economic growth and social health indicators is larger than bidirectional or neutrality hypothesis.     

Enduring Effects of Women's Early Employment Experiences on Child-spacing: The Canadian Evidence

This study examines the effects of early work experiences on child-spacing among Canadian women, with data from the 1984 Family History Survey conducted by Statistics Canada. The analyses, based on life-table and proportional hazards models, show that longer and less interrupted early work experiences are associated with longer birth intervals, and that these effects tend to persist throughout the childbearing years. The study further shows that these effects are greater on the third birth interval than on the second, and that they are more pronounced among highly educated than among less educated women.     

Shrinkage Estimators of Intercept Parameters of Two Simple Regression Models with Suspected Equal Slopes

Estimators of the intercept parameter of a simple linear regression model involves the slope estimator. In this article, we consider the estimation of the intercept parameters of two linear regression models with normal errors, when it is a priori suspected that the two regression lines are parallel, but in doubt. We also introduce a coefficient of distrust as a measure of degree of lack of trust on the uncertain prior information regarding the equality of two slopes. Three different estimators of the intercept parameters are defined by using the sample data, the non sample uncertain prior information, an appropriate test statistic, and the coefficient of distrust. The relative performances of the unrestricted, shrinkage restricted and shrinkage preliminary test estimators are investigated based on the analyses of the bias and risk functions under quadratic loss. If the prior information is precise and the coefficient of distrust is small, the shrinkage preliminary test estimator overperforms the other estimators. An example based on a medical study is used to illustrate the method.     

Estimation of Slope for Linear Regression Model with Uncertain Prior Information and Student-t Error

This article considers estimation of the slope parameter of the linear regression model with Student-t errors in the presence of uncertain prior information on the value of the unknown slope. Incorporating uncertain non sample prior information with the sample data the unrestricted, restricted, preliminary test, and shrinkage estimators are defined. The performances of the estimators are compared based on the criteria of unbiasedness and mean squared errors. Both analytical and graphical methods are explored. Although none of the estimators is uniformly superior to the others, if the non sample information is close to its true value, the shrinkage estimator over performs the rest of the estimators.     

On Estimating the Finite Population Mean Using Two Auxiliary Variables in Two Phase Sampling in the Presence of Non Response

This article considers the problem of estimating the population mean using information on two auxiliary variables in the presence of non response under two-phase sampling. Some improved ratio-in-regression type estimators have been proposed in four different situations of non response along with their properties under large sample approximation. Efficiency comparisons of the proposed estimators with the usual unbiased estimator by Hansen and Hurwitz (1946 Hansen , M. H. , Hurwitz , W. N. ( 1946 ). The problem of non response in sample surveys . J. Amer. Statist. Assoc. 41 : 517 – 529 .[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]), conventional ratio and regression estimators using single auxiliary variable and Singh and Kumar (2010b Singh , H. P. , Kumar , S. ( 2010b ). Improved estimation of population mean under double sampling with sub-sampling the non-respondents . J. Statist. Plann. Infer. 140 ( 9 ): 2536 – 2550 .[Crossref], [Web of Science ®] , [Google Scholar]) estimators using two auxiliary variables have been made. Finally, these theoretical findings are illustrated by a numerical example.     

Shrinkage Estimation Under Multivariate Elliptic Models

The estimation of the location vector of a p-variate elliptically contoured distribution (ECD) is considered using independent random samples from two multivariate elliptically contoured populations when it is apriori suspected that the location vectors of the two populations are equal. For the setting where the covariance structure of the populations is the same, we define the maximum likelihood, Stein-type shrinkage and positive-rule shrinkage estimators. The exact expressions for the bias and quadratic risk functions of the estimators are derived. The comparison of the quadratic risk functions reveals the dominance of the Stein-type estimators if p ≥ 3. A graphical illustration of the risk functions under a “typical” member of the elliptically contoured family of distributions is provided to confirm the analytical results.     

Influence diagnostics in constrained general linear models

ABSTRACT

Constrained general linear models (CGLMs) have wide applications in practice. Similar to other data analysis, the identification of influential observations that may be potential outliers is an important step beyond in the CGLMs. We develop multiple case-deletion diagnostics for detecting influential observations in the CGLMs. The diagnostics are functions of basic building blocks: studentized residuals, error contrast matrix, and the inverse of the response variable covariance matrix. The basic building blocks are computed only once from the complete data analysis and provide information on the influence of the data on different aspects of the model fit. Computational formulas are given which make the procedures feasible. An illustrative example with a real data set is also reported.