Chemometric techniques in multivariate statistical modelling of process plant

The techniques of principal component analysis (PCA) and partial least squares (PLS) are introduced from the point of view of providing a multivariate statistical method for modelling process plants. The advantages and limitations of PCA and PLS are discussed from the perspective of the type of data and problems that might be encountered in this application area. These concepts are exemplified by two case studies dealing first with data from a continuous stirred tank reactor (CSTR) simulation and second a literature source describing a low-density polyethylene (LDPE) reactor simulation.

A statistical process control approach for automatic anti-islanding detection using synchrophasors

Anti-islanding protection is becoming increasingly important due to the rapid installation of distributed generation from renewable resources like wind, tidal and wave, solar PV, bio-fuels, as well as from other resources like diesel. Unintentional islanding presents a potential risk for damaging utility plants and equipment connected from the demand side, as well as to public and personnel in utility plants. This paper investigates automatic islanding detection. This is achieved by deploying a statistical process control approach for fault detection with the real-time data acquired through a wide area measurement system, which is based on Phasor Measurement Unit (PMU) technology. In particular, the principal component analysis (PCA) is used to project the data into principal component subspace and residual space, and two statistics are used to detect the occurrence of fault. Then a fault reconstruction method is used to identify the fault and its development over time. The proposed scheme has been used in a real system and the results have confirmed that the proposed method can correctly identify the fault and islanding site.

A feasibility study testing four hypotheses with phase II outcomes in advanced colorectal cancer (MRC FOCUS3): a model for randomised controlled trials in the era of personalised medicine?

Background: Molecular characteristics of cancer vary between individuals. In future, most trials will require assessment of biomarkers to allocate patients into enriched populations in which targeted therapies are more likely to be effective. The MRC FOCUS3 trial is a feasibility study to assess key elements in the planning of such studies.

Patients and methods: Patients with advanced colorectal cancer were registered from 24 centres between February 2010 and April 2011. With their consent, patients' tumour samples were analysed for KRAS/BRAF oncogene mutation status and topoisomerase 1 (topo-1) immunohistochemistry. Patients were then classified into one of four molecular strata; within each strata patients were randomised to one of two hypothesis-driven experimental therapies or a common control arm (FOLFIRI chemotherapy). A 4-stage suite of patient information sheets (PISs) was developed to avoid patient overload.

Results: A total of 332 patients were registered, 244 randomised. Among randomised patients, biomarker results were provided within 10 working days (w.d.) in 71%, 15 w.d. in 91% and 20 w.d. in 99%. DNA mutation analysis was 100% concordant between two laboratories. Over 90% of participants reported excellent understanding of all aspects of the trial. In this randomised phase II setting, omission of irinotecan in the low topo-1 group was associated with increased response rate and addition of cetuximab in the KRAS, BRAF wild-type cohort was associated with longer progression-free survival.

Conclusions: Patient samples can be collected and analysed within workable time frames and with reproducible mutation results. Complex multi-arm designs are acceptable to patients with good PIS. Randomisation within each cohort provides outcome data that can inform clinical practice.

When does information about causal structure improve statistical reasoning?

Base rate neglect on the mammography problem can be overcome by explicitly presenting a causal basis for the typically vague false-positive statistic. One account of this causal facilitation effect is that people make probabilistic judgements over intuitive causal models parameterized with the evidence in the problem. Poorly defined or difficult-to-map evidence interferes with this process, leading to errors in statistical reasoning. To assess whether the construction of parameterized causal representations is an intuitive or deliberative process, in Experiment 1 we combined a secondary load paradigm with manipulations of the presence or absence of an alternative cause in typical statistical reasoning problems. We found limited effects of a secondary load, no evidence that information about an alternative cause improves statistical reasoning, but some evidence that it reduces base rate neglect errors. In Experiments 2 and 3 where we did not impose a load, we observed causal facilitation effects. The amount of Bayesian responding in the causal conditions was impervious to the presence of a load (Experiment 1) and to the precise statistical information that was presented (Experiment 3). However, we found less Bayesian responding in the causal condition than previously reported. We conclude with a discussion of the implications of our findings and the suggestion that there may be population effects in the accuracy of statistical reasoning.

Whose statistical reasoning is improved by information about causal structure?

People often struggle when making Bayesian probabilistic estimates on the basis of competing sources of statistical evidence. Recently, Krynski and Tenenbaum (Journal of Experimental Psychology: General, 136, 430–450, 2007) proposed that a causal Bayesian framework accounts for peoples’ errors in Bayesian reasoning and showed that, by clarifying the causal relations among the pieces of evidence, judgments on a classic statistical reasoning problem could be significantly improved. We aimed to understand whose statistical reasoning is facilitated by the causal structure intervention. In Experiment 1, although we observed causal facilitation effects overall, the effect was confined to participants high in numeracy. We did not find an overall facilitation effect in Experiment 2 but did replicate the earlier interaction between numerical ability and the presence or absence of causal content. This effect held when we controlled for general cognitive ability and thinking disposition. Our results suggest that clarifying causal structure facilitates Bayesian judgments, but only for participants with sufficient understanding of basic concepts in probability and statistics.