Spatial Sampling Design for a Demographic and Health Survey

The recent advances in global position systems (GPS), geographic information systems (GIS), and remote sensing (RS) can be exploited for spatial sampling design for demographic and health surveys. These technologies are particularly useful when a sampling frame is unavailable and/or location (of household) is important for data collection (e.g., location of residence might greatly impact exposure to ambient air pollution among members of a population). Building on these technologies, this article presents a methodology of spatial sampling adopted for the respiratory health and demographic survey conducted in Delhi and its environs from January through April 2004. The overall goal of the survey was to select households that adequately represented exposure to ambient air pollution. The proposed methodology involved constructing a sampling frame of residential areas and the simulation of weighted random points within residential areas. The simulated locations were navigated with the aid of GPS to identify households at these locations and to acquire residents’ consent to participate in the survey; a total of 1,576 households at the 2,000 simulated locations were found suitable and participated in the survey. The average ambient air pollution at the sample sites was not significantly different from the average air pollution observed in the study area, which demonstrates the robustness of the proposed sampling method.     

Estimation of smooth regression functions in monotone response models

We consider the estimation of smooth regression functions in a class of conditionally parametric co-variate-response models. Independent and identically distributed observations are available from the distribution of (Z,X)$(Z,X)$, where Z is a real-valued co-variate with some unknown distribution, and the response X conditional on Z   is distributed according to the density p(·,ψ(Z))$p(·,\psi (Z))$, where p(·,θ)$p(·,\theta )$ is a one-parameter exponential family. The function ψ$\psi$ is a smooth monotone function. Under this formulation, the regression function E(X|Z)$E(X|Z)$ is monotone in the co-variate Z   (and can be expressed as a one–one function of ψ$\psi$); hence the term “monotone response model”. Using a penalized least squares approach that incorporates both monotonicity and smoothness, we develop a scheme for producing smooth monotone estimates of the regression function and also the function ψ$\psi$ across this entire class of models. Point-wise asymptotic normality of this estimator is established, with the rate of convergence depending on the smoothing parameter. This enables construction of Wald-type (point-wise) as well as pivotal confidence sets for ψ$\psi$ and also the regression function. The methodology is extended to the general heteroscedastic model, and its asymptotic properties are discussed.     

CONFLICT RESOLUTION IN INVESTMENT DECISIONS: IMPLEMENTATION OF GOAL PROGRAMMING METHODOLOGY FOR DUAL-PURPOSE FUNDS*

Goal programming (GP) is designed to resolve allocation problems with conflicting goals. Both goals and constraints are incorporated in the allocational decision, and the objective function is stated in a way that, upon solution, yields a result “as close as possible” to the priority-weighted goals. The present paper applies GP methodology to the investment decision of dual-purpose funds (DPFs), that are required by law to pursue allocational decisions with potentially conflicting objectives. It provides an empirical demonstration that DPF managers could have improved their investment selection and subsequent performance by the use of GP methodology. Finally the paper stresses the importance of sensitivity analysis to improve both the goal-ranking and target-selection aspects of the methodology and provides a limited but illuminating empirical demonstration of post-optimality analysis.     

Proportional odds model – a quantile approach

The paper discusses a quantile-based definition for the well-known proportional odds model. We present various reliability properties of the model using quantile functions. Different ageing properties are derived. A generalization for the class of distributions with bilinear hazard quantile function is established and the practical application of this model is illustrated with a real-life data set.     

Operationalising lean in healthcare: the impact of professionalism

Abstract

Lean is endorsed as policy in practice in the UK but the challenges and complexities affecting Lean in healthcare are still to be adequately assessed. Through a qualitative single case study of an NHS organisation implementing Lean, 43 interviews with multi-disciplinary team members involved in Lean were conducted. The progress of Lean is found to be inhibited as medical professionals have failed to engage or provide clinical leadership in supporting the trajectory of Lean. This resulted in limited outcomes, sustainability implications, and failed projects. Lean is challenged by complexity and this is evident in conflicts between professional identity, corresponding status and clinical/managerial relationships. Medical professionals as a group have received a limited focus in papers assessing the progress of Lean in Healthcare from an operational perspective. Going forward, strategies for mitigating the negative impact of this can be developed to support operational managers in the healthcare domain.     

Optimal inventory cycle counting

The heart of inventory transactions management involves obtaining an accurate count of on-hand inventories employing different procedures. Among such procedures cycle counting is said to provide better inventory record accuracy for financial control and production planning. However, these procedures use rather arbitrary basis for classifying items and for setting the count frequency within each strata. This paper develops a simple optimization model of inventory cycle counting based on traditional stratified sampling theory. Such a model allows the inventory manager to make statistically supported statements regarding inventory record accuracy so as to meet financial control requirements with a minimum of cycle counting effort. A numerical example is included to illustrate the model.     

Addressing issues of face validity in the application of a clinical guideline

In 1995, a statewide project to improve the practice of electroconvulsive therapy (ECT) in the Medicare population was started in Louisiana. A retrospective baseline study found high concurrent utilization of lithium and benzodiazepines, medications considered detrimental to ECT by the second American Psychiatric Association Task Force on ECT. Resistance to a clinical guideline because of lack of face validity by clinicians was used as an opportunity to involve clinicians in using data to evaluate their clinical practices. Results of the first two quarters of the project demonstrated that the strategy was successful.     

Internationalization,Innovation and Institutions: The 3 I's Underpinning the Competitiveness of Emerging Market Firms

The past decade has seen an increase in the extent of research focused on and around emerging market firms (EMFs) and their rising levels of competitiveness in both their home markets and more importantly in the global market place. At the same time, the practitioner-oriented literature has been documenting a growing number of corporate success stories that originate in emerging market economies. We posit that the growing prominence of EMFs is a result of three interrelated phenomena: the fast-paced internationalization of EMFs into both developing and developed market economies; the rapidly increasing extent to which business enterprises in emerging economies are focusing on knowledge-intensive processes and innovation; and the continuous evolution of institutions in these markets, particularly in terms of economic liberalization.     

Alternative estimators for the population totals in multiple characteristic surveys

The problem of estimating the population totals of multiple characteristics using without replacement sampling design is addressed. The alternative estimators for the study characteristics which are unrelated or have the low correlations with the characteristic used in sample selection are suggested. The efficiency of the estimators under two super population models, approximating the situations in which both the characteristics are unrelated and also when they are related, is studed. A numerical investigation is also carried out to get insight into the performance of the estimators in real applications.