Discussion of "Impact of Frequentist and Bayesian Methods on Survey Sampling Practice: A Selective Appraisal" by J. N. K. Rao

This comment emphasizes the importance of model checking and model fitting when making inferences about finite population quantities. It also suggests the value of using unit level models when making inferences for small subpopulations, that is, "small area" analyses [arXiv:1108.2356].Comment: Published in at http://dx.doi.org/10.1214/11-STS346B the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

A Case Study of Nonresponse Bias Analysis

Nonresponse bias is a widely prevalent problem for data collections. We develop a ten-step exemplar to guide nonresponse bias analysis (NRBA) in cross-sectional studies, and apply these steps to the Early Childhood Longitudinal Study, Kindergarten Class of 2010-11. A key step is the construction of indices of nonresponse bias based on proxy pattern-mixture models for survey variables of interest. A novel feature is to characterize the strength of evidence about nonresponse bias contained in these indices, based on the strength of relationship of the characteristics in the nonresponse adjustment with the key survey variables. Our NRBA incorporates missing at random and missing not at random mechanisms, and all analyses can be done straightforwardly with standard statistical software

Make Research Data Public? -- Not Always so Simple: A Dialogue for Statisticians and Science Editors

Putting data into the public domain is not the same thing as making those data accessible for intelligent analysis. A distinguished group of editors and experts who were already engaged in one way or another with the issues inherent in making research data public came together with statisticians to initiate a dialogue about policies and practicalities of requiring published research to be accompanied by publication of the research data. This dialogue carried beyond the broad issues of the advisability, the intellectual integrity, the scientific exigencies to the relevance of these issues to statistics as a discipline and the relevance of statistics, from inference to modeling to data exploration, to science and social science policies on these issues.Comment: Published in at http://dx.doi.org/10.1214/10-STS320 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

The development and validation of a novel, parameter-free, modelling strategy for electromembrane processes: Electrodialysis

As the global water crisis worsens and natural resources of strategic inorganic elements dwindle, the need for efficient and effective salt separation methods is becoming ever more important. Electromembrane processes, and in particular electrodialysis, are emerging as efficient and effective separation technologies that use an electric field to drive the transport of ions against a concentration gradient. Modelling electromembrane processes allows for process design and optimisation, as well as the identification of what technological improvements would have the greatest effect. However, the wide use of empirical fitting parameters in most existing models greatly limits their globality. The presence of complex and confounding phenomena within electromembrane processes greatly exacerbates this. In this work, a novel, circuit-based modelling strategy for electromembrane processes is presented, avoiding the use of any fitting parameters. Conventional electrodialysis is adopted as a case study. The implementation of a novel transport number model and membrane resistance model are crucial for model accuracy over a wide range of process conditions. The model was experimentally validated and showed excellent agreement with experimental data across a range of concentrations and voltages. Consequently, this model will prove to be an excellent tool for researchers and process designers

The development and evaluation of a parameter-free circuit-based model of bipolar membrane electrodialysis for process design and optimisation

Bipolar membrane electrodialysis (BPMED) is an emerging electromembrane technology which has the potential to replace existing pH manipulation process units among others and take advantage of the benefits posed by process electrification. The development of robust and flexible process models of BPMED for design and optimisation is paramount in derisking potential instillations and improving commercial viability. Herein, a circuit-based model of BPMED is presented which avoids reliance on empirical fitting parameters and training data. The resulting model is flexible enough that extension to account for added complexities may be readily adopted. The mass transfer and electrical resistance of six different domains (three membranes and three streams) were computed by applying fundamental laws such as Ohm’s law and Faraday’s first law. Acid-base reactions and their effect releasing current within the membranes were also considered. Furthermore, the stack model can be readily embedded in a broader process model. To this end, the stack model is applied to a recirculating-batch experiment using a delayed differential material balance to account for dead-time within the tubing and measurement flow-cells. Two orthogonal methods of experimental validation were conducted to assess the performance of the model over a range of concentrations and applied voltages. These involved running a recirculating-batch experiment and collecting current–voltage polarisation data, respectively, and both showed good agreement with the model predictions. Overall, a robust model of BPMED has been produced which is able to accurately predict system performance and will prove useful for the design and optimisation of industrial systems

Imputation of missing values using density estimation

The paper proposes the use of density estimation, based on respondents' data, for imputing values for nonrespondents. Multiple imputations are catered for and the procedure is assessed on the basis of estimation of the underlying density itself. Extension to Rubin's version of multiple imputations is described.kernel method multiple imputations smoothed bootstrap