Impact of contamination on empirical and theoretical error

Classification analysis allows to group similar objects into a given number of groups by means of a classification rule. Many classification procedures are available : linear discrimination, logistic discrimination, etc. Focus in this poster will be on classification resulting from a clustering analysis. Indeed, among the outputs of classical clustering techniques, a classification rule is provided in order to classify the objects into one of the clusters. More precisely, let F denote the underlying distribution and assume that the generalized kmeans algorithm with penalty function is used to construct the k clusters C1(F), . . . ,Ck(F) with centers T1(F), . . . , Tk(F). When one feels that k true groups are existing among the data, classification might be the main objective of the statistical analysis. Performance of a particular classification technique can be measured by means of an error rate. Depending on the availability of data, two types of error rates may be computed: a theoretical one and a more empirical one. In the first case, the rule is estimated on a training sample with distribution F while the evaluation of the classification performance may be done through a test sample distributed according to a model distribution of interest, Fm say. In the second case, the same data are used to set up the rule and to evaluate the performance. Under contamination, one has to replace the distribution F of the training sample by a contaminated one, F(eps) say (where eps corresponds to the fraction of contamination). In that case, thetheoretical error rate will be corrupted since it relies on a contaminated rule but it may still consider a test sample distributed according to the model distribution. The empirical error rate will be affected twice: via the rule and also via the sample used for the evaluation of the classification performance. To measure the robustness of classification based on clustering, influence functions of the error rate may be computed. The idea has already been exploited by Croux et al (2008) and Croux et al (2008) in the context of linear and logistic discrimination. In the computation of influence functions, the contaminated distribution takes the form F(eps) = (1 − eps)*Fm + eps* Dx, where Dx is the Dirac distribution putting all its mass at x. It is interesting to note that the impact of the point mass x may be positive, i.e. may decrease the error rate, when the data at hand is used to evaluate the error

Methodological bias in cluster randomised trials

Background: Cluster randomised trials can be susceptible to a range of methodological problems. These problems are not commonly recognised by many researchers. In this paper we discuss the issues that can lead to bias in cluster trials. Methods: We used a sample of cluster randomised trials from a recent review and from a systematic review of hip protectors. We compared the mean age of participants between intervention groups in a sample of 'good' cluster trials with a sample of potentially biased trials. We also compared the effect sizes, in a funnel plot, between hip protector trials that used individual randomisation compared with those that used cluster randomisation. Results: There is a tendency for cluster trials, with evidence methodological biases, to also show an age imbalance between treatment groups. In a funnel plot we show that all cluster trials show a large positive effect of hip protectors whilst individually randomised trials show a range of positive and negative effects, suggesting that cluster trials may be producing a biased estimate of effect. Conclusion: Methodological biases in the design and execution of cluster randomised trials is frequent. Some of these biases associated with the use of cluster designs can be avoided through careful attention to the design of cluster trials. Firstly, if possible, individual allocation should be used. Secondly, if cluster allocation is required, then ideally participants should be identified before random allocation of the clusters. Third, if prior identification is not possible, then an independent recruiter should be used to recruit participants

Deep Multi-object Spectroscopy to Enhance Dark Energy Science from LSST

Community access to deep (i ~ 25), highly-multiplexed optical and near-infrared multi-object spectroscopy (MOS) on 8-40m telescopes would greatly improve measurements of cosmological parameters from LSST. The largest gain would come from improvements to LSST photometric redshifts, which are employed directly or indirectly for every major LSST cosmological probe; deep spectroscopic datasets will enable reduced uncertainties in the redshifts of individual objects via optimized training. Such spectroscopy will also determine the relationship of galaxy SEDs to their environments, key observables for studies of galaxy evolution. The resulting data will also constrain the impact of blending on photo-z's. Focused spectroscopic campaigns can also improve weak lensing cosmology by constraining the intrinsic alignments between the orientations of galaxies. Galaxy cluster studies can be enhanced by measuring motions of galaxies in and around clusters and by testing photo-z performance in regions of high density. Photometric redshift and intrinsic alignment studies are best-suited to instruments on large-aperture telescopes with wider fields of view (e.g., Subaru/PFS, MSE, or GMT/MANIFEST) but cluster investigations can be pursued with smaller-field instruments (e.g., Gemini/GMOS, Keck/DEIMOS, or TMT/WFOS), so deep MOS work can be distributed amongst a variety of telescopes. However, community access to large amounts of nights for surveys will still be needed to accomplish this work. In two companion white papers we present gains from shallower, wide-area MOS and from single-target imaging and spectroscopy.Comment: Science white paper submitted to the Astro2020 decadal survey. A table of time requirements is available at http://d-scholarship.pitt.edu/36036

The Dark Energy Survey

We describe the Dark Energy Survey (DES), a proposed optical-near infrared survey of 5000 sq. deg of the South Galactic Cap to ~24th magnitude in SDSS griz, that would use a new 3 sq. deg CCD camera to be mounted on the Blanco 4-m telescope at Cerro Telolo Inter-American Observatory (CTIO). The survey data will allow us to measure the dark energy and dark matter densities and the dark energy equation of state through four independent methods: galaxy clusters, weak gravitational lensing tomography, galaxy angular clustering, and supernova distances. These methods are doubly complementary: they constrain different combinations of cosmological model parameters and are subject to different systematic errors. By deriving the four sets of measurements from the same data set with a common analysis framework, we will obtain important cross checks of the systematic errors and thereby make a substantial and robust advance in the precision of dark energy measurements.Comment: White Paper submitted to the Dark Energy Task Force, 42 page

A simple state-based prognostic model for filter clogging

In today's maintenance planning, fuel filters are replaced or cleaned on a regular basis. Monitoring and implementation of prognostics on filtration system have the potential to avoid costs and increase safety. Prognostics is a fundamental technology within Integrated Vehicle Health Management (IVHM). Prognostic models can be categorised into three major categories: 1) Physics-based models 2) Data-driven models 3) Experience-based models. One of the challenges in the progression of the clogging filter failure is the inability to observe the natural clogging filter failure due to time constraint. This paper presents a simple solution to collect data for a clogging filter failure. Also, it represents a simple state-based prognostic with duration information (SSPD) method that aims to detect and forecast clogging of filter in a laboratory based fuel rig system. The progression of the clogging filter failure is created unnaturally. The degradation level is divided into several groups. Each group is defined as a state in the failure progression of clogging filter. Then, the data is collected to create the clogging filter progression states unnaturally. The SSPD method consists of three steps: clustering, clustering evaluation, and remaining useful life (RUL) estimation. Prognosis results show that the SSPD method is able to predicate the RUL of the clogging filter accurately

Hand washing promotion for preventing diarrhoea.

BACKGROUND: Diarrhoea accounts for 1.8 million deaths in children in low- and middle-income countries (LMICs). One of the identified strategies to prevent diarrhoea is hand washing. OBJECTIVES: To assess the effects of hand washing promotion interventions on diarrhoeal episodes in children and adults. SEARCH METHODS: We searched the Cochrane Infectious Diseases Group Specialized Register (27 May 2015); CENTRAL (published in the Cochrane Library 2015, Issue 5); MEDLINE (1966 to 27 May 2015); EMBASE (1974 to 27 May 2015); LILACS (1982 to 27 May 2015); PsycINFO (1967 to 27 May 2015); Science Citation Index and Social Science Citation Index (1981 to 27 May 2015); ERIC (1966 to 27 May 2015); SPECTR (2000 to 27 May 2015); Bibliomap (1990 to 27 May 2015); RoRe, The Grey Literature (2002 to 27 May 2015); World Health Organization (WHO) International Clinical Trial Registry Platform (ICTRP), metaRegister of Controlled Trials (mRCT), and reference lists of articles up to 27 May 2015. We also contacted researchers and organizations in the field. SELECTION CRITERIA: Individually randomized controlled trials (RCTs) and cluster-RCTs that compared the effects of hand washing interventions on diarrhoea episodes in children and adults with no intervention. DATA COLLECTION AND ANALYSIS: Three review authors independently assessed trial eligibility, extracted data, and assessed risk of bias. We stratified the analyses for child day-care centres or schools, community, and hospital-based settings. Where appropriate, incidence rate ratios (IRR) were pooled using the generic inverse variance method and random-effects model with 95% confidence intervals (CIs). We used the GRADE approach to assess the quality of evidence. MAIN RESULTS: We included 22 RCTs: 12 trials from child day-care centres or schools in mainly high-income countries (54,006 participants), nine community-based trials in LMICs (15,303 participants), and one hospital-based trial among people with acquired immune deficiency syndrome (AIDS) (148 participants).Hand washing promotion (education activities, sometimes with provision of soap) at child day-care facilities or schools prevents around one-third of diarrhoea episodes in high income countries (rate ratio 0.70; 95% CI 0.58 to 0.85; nine trials, 4664 participants, high quality evidence), and may prevent a similar proportion in LMICs but only two trials from urban Egypt and Kenya have evaluated this (rate ratio 0.66, 95% CI 0.43 to 0.99; two trials, 45,380 participants, low quality evidence). Only three trials reported measures of behaviour change and the methods of data collection were susceptible to bias. In one trial from the USA hand washing behaviour was reported to improve; and in the trial from Kenya that provided free soap, hand washing did not increase, but soap use did (data not pooled; three trials, 1845 participants, low quality evidence).Hand washing promotion among communities in LMICs probably prevents around one-quarter of diarrhoea episodes (rate ratio 0.72, 95% CI 0.62 to 0.83; eight trials, 14,726 participants, moderate quality evidence). However, six of these eight trials were from Asian settings, with only single trials from South America and sub-Saharan Africa. In six trials, soap was provided free alongside hand washing education, and the overall average effect size was larger than in the two trials which did not provide soap (soap provided: rate ratio 0.66, 95% CI 0.56 to 0.78; six trials, 11,422 participants; education only: rate ratio: 0.84, 95% CI 0.67 to 1.05; two trials, 3304 participants). There was increased hand washing at major prompts (before eating/cooking, after visiting the toilet or cleaning the baby's bottom), and increased compliance to hand hygiene procedure (behavioural outcome) in the intervention groups than the control in community trials (data not pooled: three trials, 3490 participants, high quality evidence).Hand washing promotion for the one trial conducted in a hospital among high-risk population showed significant reduction in mean episodes of diarrhoea (1.68 fewer) in the intervention group (Mean difference 1.68, 95% CI 1.93 to 1.43; one trial, 148 participants, moderate quality evidence). There was increase in hand washing frequency, seven times per day in the intervention group versus three times in the control in this hospital trial (one trial, 148 participants, moderate quality evidence).We found no trials evaluating or reporting the effects of hand washing promotions on diarrhoea-related deaths, all-cause-under five mortality, or costs. AUTHORS' CONCLUSIONS: Hand washing promotion probably reduces diarrhoea episodes in both child day-care centres in high-income countries and among communities living in LMICs by about 30%. However, less is known about how to help people maintain hand washing habits in the longer term