Search results

The aim of this paper is to propose alternative sampling schemes for indoor air quality assessments in which a balance between the sampling time required and number of sampling points chosen is allowed.

CO₂ was used as a reference to evaluate the existing and the proposed sampling schemes for average indoor pollutant concentration regarding the sampling time and sampling point density required and the probable errors induced at certain confidence levels of the measurement.

The results showed that increasing the number of sampling points would significantly reduce the corresponding sampling time required: at the same confidence level, doubling the sampling point density from 250 m² to 500 m² per measurement point would reduce the sampling time required from eight hours to three hours.

The model parameters were determined from surveys for some typical offices in Hong Kong and therefore may require adaptation for use elsewhere.

A useful source of information in determining the number of sampling points chosen and the duration of the measurement needed according to the “acceptable” measurement uncertainty allowed.

Providing flexibility in IAQ assessment, the results would be useful to policymakers for making decisions on resources and manpower management for their respective sampling strategies while taking the uncertainties into account.

The purpose of this paper is to investigate a new sampling methodology previously proposed for systems with a known lifetime distribution: the Predetermined Sampling Intervals (PSI) method.

The methodology is defined on basis of system hazard cumulative rate, and is compared with other approaches, particularly those whose parameters may change in real time, taking into account current sample information.

For different lifetime distributions, the results obtained for adjusted average time to signal (AATS) using a control chart for the sample mean are presented and analysed. They demonstrate the high degree of statistical performance of this sampling procedure, particularly when used in systems with an increasing failure rate distribution.

This PSI method is important from a quality and reliability management point of view.

This methodology involves a process by which sampling instants are obtained at the beginning of the process to be controlled. Also this new approach allows for statistical comparison with other sampling schemes, which is a novel feature.

The purpose of this paper is to examine commonly relied upon product sampling strategies, direct‐to‐consumer sampling and event sampling, to determine which method can deliver the greatest return on investment in a variety of situations.

The paper explores information and data collected via case studies in actual programs presented to brands. The trial, sample, and purchase numbers were actual averages from sampling effectiveness studies for these types of programs.

The paper identifies and segments different types of products and the method by which the products are most effectively implemented into trial and sampling programs. ICOM reveals hard statistics on the return on investment of programs utilizing multiple methods of sampling including point‐of‐use, direct mail and event sampling.

Marketers should follow the STEPS outlined in this study to apply the best brand sampling strategy for a given product. Knowledge of targeted consumer base along with careful pre‐event analysis will deliver the best return on investment for a trial campaign.

The paper reveals reasons for a growing shift in corporate budgeted marketing dollars from event marketing to direct consumer product sampling. While event sampling is not always an ineffective or inferior marketing method,the reader can discover methods for a pre‐event return on investment analysis that will reveal the sampling strategy sure to deliver the most “bang” for the marketing “buck”.

The most frequently used attribute sampling plan in MIL‐STD 105 D. In cases, however, when the quality level of incoming lots is generally sufficiently good, MIL‐STD 105 D often leads to unnecessarily high sampling cost. This can be avoided by using α‐optimal sampling plans. The authors outline the α‐optimal sampling scheme along with a simple procedure to determine α‐optimal sampling plans at workshop level. These plans depend on three parameters which have to be estimated from recorded data. In this article the effects of estimation errors in these parameters are investigated.

Importance sampling is a popular Monte Carlo method used in a variety of areas in econometrics. When the variance of the importance sampling estimator is infinite, the central limit theorem does not apply and estimates tend to be erratic even when the simulation size is large. The authors consider asymptotic trimming in such a setting. Specifically, the authors propose a bias-corrected tail-trimmed estimator such that it is consistent and has finite variance. The authors show that the proposed estimator is asymptotically normal, and has good finite-sample properties in a Monte Carlo study.

Health care insurance companies often conduct sample surveys of health plan members. Survey purposes include: consumer satisfaction with the plan and members’ health status, functional status, health literacy and/or health services utilization outside of the plan. Vendors or contractors typically conduct these surveys for insurers. Survey results may be used for plans’ accreditation, evaluation, quality improvement and/or marketing. This article describes typical sampling plans and data analysis strategies used in these surveys, showing how these methods may result in biased estimators of population parameters (e.g. percentage of plan members who are satisfied). Practical suggestions are given to improve these surveys: alternate sampling plans, increasing the response rate, component calculation for the survey response rate, weighted analyses, and adjustments for unit non-response. Since policy, regulation, accreditation, management and marketing decisions are based, in part, on results from these member surveys, these important and numerous surveys need to be of higher quality.

The commitment to statistical process control programmes is becoming commonplace in US industry. However, some companies are experiencing failure of these programmes, particularly in multi‐strata (population) production processes. Even if such a process is in a state of statistical control, there is a high likelihood that one or more strata could drift away from the target owing to an assignable cause. The success of a QC programme depends on the ability of a quality control practitioner to detect this shift with a greater statistical power (sensitivity) and take corrective actions. Addresses the problem faced by the multi‐strata production process of a local manufacturing company in detecting a single stratum shift from the target with a high level of sensitivity. Proposes the selection of an appropriate sampling method (stratified or random) to have a strong bearing on the relative sensitivity of detecting the above shift in a single stratum. Develops power curves for the above mentioned process under stratified and random sampling scenarios, when a shift occurs in a single stratum. Examines the relationship of sample size to the threshold level of the stratum shift and the preferred sampling method.

The purpose of this paper is to provide a rigorous, statistically correct, and low‐cost way to audit sample a lender's loan portfolio, be they a microlender or other type of lender. No other paper applies this method to loan portfolios, even though it is a high demand application.

Standard techniques of audit sampling and dollar unit sampling with stratification are applied to the particular case of a microlender's portfolio. Unlike the audit sampling that almost all auditors use, no arbitrary rules of thumb are applied.

The paper finds that statistical audit sampling for a lender's loan portfolio is simple, rigorous, and inexpensive.

In audit sampling, most auditors use arbitrary rules of thumb and have no idea whether they are sampling enough items to actually be sure, with some desired level of confidence, that they have found no defects. This simple, inexpensive, and statistically rigorous technique will allow auditors who actually want to do a good job to quantify the precision of their statements in a very common application.

This paper combines several disparate threads from the statistical literature on audit sampling in a way that auditors (who are usually not statisticians) can apply them for auditing the quality of a lender's portfolio – microfinance or otherwise – which is a very common need.

The red bead experiment (RBE) is very well‐known in total quality management. It was designed by Dr W.E. Deming to show managers several lessons in management and sampling. The RBE has evolved over several decades and now has an accepted standard format. The RBE also serves as an introduction to control charts which are an important tool in statistical quality control. Surprisingly very little is known about the statistical properties of the RBE. This is most likely due to the fact that the RBE involves mechanical sampling and so is much more complicated than probability sampling. New methods are given for statistical analysis when mechanical sampling is involved (e.g. mechanical sampling of non‐uniform output in a manufacturing environment). This approach has specific applications to control charts which are a major tool in quality control. In a rigorous analysis, it is shown that p, the sample proportion of red beads in the RBE, is an unbiased estimator of the RBE process defect rate. Expressions are also found for the variance of p, enabling the calculation of control chart limits. Some of the techniques used can be applied to other mechanical sampling situations which may at first viewing seem intractable.