Control Charts and the Effect of the Two-Component Measurement Error Model

Monitoring algorithms, such as the Shewhart and Cusum control charts, are often used for monitoring purposes in the chemical industry or within an environmental context. The statistical properties of these algorithms are known to be highly responsive to measurement errors. Recent studies have underlined the important role played by the twocomponent measurement error model in chemical and environmental monitoring. In the present work, we study the effects of the twocomponent error model on the performance of the X and S Shewhart control charts. Results reveal that gauge imprecision may seriously alter the statistical properties of the control charts. We propose how to reduce the effects of measurement errors, and illustrate how to take errors into account in the design of monitoring algorithmsAverage run length, calibration curve, constant measurement error, Monte Carlo study, proportional measurement error, repeated measurements, Shewhart control charts

Finite population properties of predictors based on spatial patterns

When statistical inference is used for spatial prediction, the model-based framework known as kriging is commonly used. The predictor for an unsampled element of a population is a weighted combination of sampled values, in which weights are obtained by estimating the spatial covariance function. This solution can be affected by model misspecification and can be influenced by sampling design properties. In classical design-based finite population inference, these problems can be overcome; nevertheless, spatial solutions are still seldom used for this purpose. Through the efficient use of spatial information, a conceptual framework for design-based estimation has been developed in this study. We propose a standardized weighted predictor for unsampled spatial data, using the population information regarding spatial locations directly in the weighting system. Our procedure does not require model estimation of the spatial pattern because the spatial relationship is captured exclusively based on the Euclidean distances between locations (which are fixed and do not require assessment after sample selection). The individual predictor is a design-based ratio estimator, and we illustrate its properties for simple random sampling.spatial sampling; ratio estimator; design-based inference; model-based inference; spatial information in finite population inference campionamento spaziale, stimatore del rapporto, inferenza da disegno, inferenza da modello; informazione spaziale nell’inferenza da popolazioni finite

A Bayesian Hierarchical Approach to Ensemble Weather Forecasting

In meteorology, the traditional approach to forecasting employs deterministic models mimicking atmospheric dynamics. Forecast uncertainty due to the partial knowledge of initial conditions is tackled by Ensemble Predictions Systems (EPS). Probabilistic forecasting is a relatively new approach which may properly account for all sources of uncertainty. In this work we propose a hierarchical Bayesian model which develops this idea and makes it possible to deal with an EPS with non-identifiable members using a suitable definition of the second level of the model. An application to Italian small-scale temperature data is shown.Ensemble Prediction System, hierarchical Bayesian model, predictive distribution, probabilistic forecast, verification rank histogram.

Control Charts and the Effect of the Two-Component Measurement Error Model

Monitoring algorithms, such as the Shewhart and Cusum control charts, are often used for monitoring purposes in the chemical industry or within an environmental context. The statistical properties of these algorithms are known to be highly responsive to measurement errors. Recent studies have underlined the important role played by the twocomponent measurement error model in chemical and environmental monitoring. In the present work, we study the effects of the twocomponent error model on the performance of the X and S Shewhart control charts. Results reveal that gauge imprecision may seriously alter the statistical properties of the control charts. We propose how to reduce the effects of measurement errors, and illustrate how to take errors into account in the design of monitoring algorithms

Planning a sample for an epidemiological survey

This work illustrates the joint use of a pilot study and an administrative data base for designing a probabilistic sample for an epidemiological survey. The target is to estimate the prevalence of an asymptomatic disease, the aortic valve stenosis (AS), in the elderly population of the city of Bologna, Italy. The novelty of the study is to reach the target population of elderly patients via a sample of their general practitioners (GPs). The pilot study was conducted in San Giovanni in Persiceto, a town in the province of Bologna. Overall information on patients and their GPs are available in the Azienda Unità Sanitaria Locale di Bologna (AUSL) data sets. Since the disease is asymptomatic, the sampling plan is designed to estimate the number of suspected patients that will be sent to further echocardiographic (ECO) examination. The probabilistic sampling plan aims at controlling the sources of randomness, via an appropriate clustering of the population of GPs. The number of practitioners to sample is fixed in advance. The subpopulations of patients to screen are also defined in advance and assigned to doctors. In this way the potential sources of randomness, due to the individual choices of doctors out of the definition of the experiment, are avoided. The number of elderly patients per doctor has been identified, from the pilot study, as an important factor able to influence the proportion of suspected patients sent to further examination. This feature is the leading factor of the sampling design, together with the clustering of the AUSL Bologna territory in NCPs, which emerges from the AUSL data set

Finite population properties of predictors based on spatial patterns

When statistical inference is used for spatial prediction, the model-based framework known as kriging is commonly used. The predictor for an unsampled element of a population is a weighted combination of sampled values, in which weights are obtained by estimating the spatial covariance function. This solution can be affected by model misspecification and can be influenced by sampling design properties. In classical design-based finite population inference, these problems can be overcome; nevertheless, spatial solutions are still seldom used for this purpose. Through the efficient use of spatial information, a conceptual framework for design-based estimation has been developed in this study. We propose a standardized weighted predictor for unsampled spatial data, using the population information regarding spatial locations directly in the weighting system. Our procedure does not require model estimation of the spatial pattern because the spatial relationship is captured exclusively based on the Euclidean distances between locations (which are fixed and do not require assessment after sample selection). The individual predictor is a design-based ratio estimator, and we illustrate its properties for simple random sampling

A Bayesian Hierarchical Approach to Ensemble Weather Forecasting

In meteorology, the traditional approach to forecasting employs deterministic models mimicking atmospheric dynamics. Forecast uncertainty due to the partial knowledge of initial conditions is tackled by Ensemble Predictions Systems (EPS). Probabilistic forecasting is a relatively new approach which may properly account for all sources of uncertainty. In this work we propose a hierarchical Bayesian model which develops this idea and makes it possible to deal with an EPS with non-identifiable members using a suitable definition of the second level of the model. An application to Italian small-scale temperature data is shown