{"title":"Dead band adjustment charts with asymmetric off‐target costs, deterministic process drift and delayed dynamics","authors":"A. Luceño","doi":"10.1046/J.1467-9884.2003.00375.X","DOIUrl":"https://doi.org/10.1046/J.1467-9884.2003.00375.X","url":null,"abstract":"Summary. Feed-back control systems are a type of process control method in which a quantitative quality characteristic is measured at regular intervals of time, successive measures are used to estimate the current mean deviation of the quality characteristic from the target and, when necessary, adjustments of an input compensatory variable are made to bring the estimated process mean back to the target. Important problems to be solved are how often to sample the quality characteristic and when and by how much to apply an adjustment, i.e. to determine the optimal sampling interval, action limits and amount of adjustment. This determination may be done to minimize the long run cost resulting from the combined effects of the costs of taking each observation, of making each adjustment and of being off target. When the adjustment cost is zero, adjustments are made at each sampling point (this is a repeated adjustment scheme). However, when the cost of making an adjustment is important, two different action limits must be chosen and an adjustment must be made only when the current estimate of the mean deviation of the quality characteristic from target jumps outside the action limits (this is a dead band scheme). We consider three situations requiring asymmetric action limits; these are deterministic process drift, asymmetric off-target costs and asymmetric underlying distributions. Deterministic process drift is often caused by tool wear. Asymmetric costs may be due to asymmetric tolerances and/or to different costs of exceeding positive and negative tolerances. Asymmetric distributions sometimes appear in essentially positive quality characteristics. In addition, we consider the possibility that there is a delay in taking appropriate actions; this delay is often caused by the tests and procedures that are required to sample the process.","PeriodicalId":83973,"journal":{"name":"The Statistician : journal of the Institute of Statisticians","volume":"3 1","pages":"501-514"},"PeriodicalIF":0.0,"publicationDate":"2003-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78281345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"When is one experiment ‘always better than’ another?","authors":"P. Goel, J. Ginebra","doi":"10.1046/J.1467-9884.2003.00376.X","DOIUrl":"https://doi.org/10.1046/J.1467-9884.2003.00376.X","url":null,"abstract":"Summary. Considering the choice between two experiments E and F, an experimenter may choose one or the other depending on the optimality criteria. However, sometimes by using the observations from E, he or she can do at least as well as by using the observations from F for every decision problem, and therefore for every inference problem as well. When that happens, it is said that experiment E is ‘always better than’F or equivalently that E is ‘sufficient for’F. The paper provides a simple explanation of what is meant by this phrase and presents a variety of situations in which one experiment E is known to be always better than an alternative F. In addition, simplifying connections between various results are also revealed. Even though these issues are important to the design of statistical experiments and to the concept of statistical information, the literature reviewed here has largely failed in communicating its results across to many researchers in these areas. One of the objectives is to fill that gap, by stressing the implications of the results, while omitting most of the technicalities that are required in their proofs.","PeriodicalId":83973,"journal":{"name":"The Statistician : journal of the Institute of Statisticians","volume":"41 1","pages":"515-537"},"PeriodicalIF":0.0,"publicationDate":"2003-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76828019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Bayesian updating for binary variables: an application in the UK water industry","authors":"Michail Papathomas, R. Hocking","doi":"10.1046/J.0039-0526.2003.00451.X","DOIUrl":"https://doi.org/10.1046/J.0039-0526.2003.00451.X","url":null,"abstract":"Summary. In accordance with the regulation of the privatized English and Welsh water industry, water companies are required to submit an asset management plan every few years. A requirement of South West Water's asset management planning is to state the probability that some of its assets will need refurbishment or replacement within the following 5 years. Probability assessments are elicited for the company's assets based on information of varying quality. For the formulation and updating of beliefs we propose and employ the threshold model, a Bayesian updating procedure. The model's main aspect is Jeffrey's conditionalization. This is an updating rule based on a simple conditional independence assumption. According to this rule, it is not necessary to construct a fully specified joint density for the quantities of interest and the probability assessments.","PeriodicalId":83973,"journal":{"name":"The Statistician : journal of the Institute of Statisticians","volume":"28 1","pages":"483-499"},"PeriodicalIF":0.0,"publicationDate":"2003-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86384864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"How many samples?: A Bayesian nonparametric approach","authors":"S. Walker","doi":"10.1046/J.1467-9884.2003.00373.X","DOIUrl":"https://doi.org/10.1046/J.1467-9884.2003.00373.X","url":null,"abstract":"The paper considers a Bayesian nonparametric decision theoretic approach to sample size calculations, where the ultimate goal is to make a terminal action from a finite set of actions. This terminal action is made via the maximization of expected utility, the maximization being made with respect to a probability measure on the states of nature. The probability measure depends on the amount of information, i.e. the number of samples collected. It is the prior in the case of no samples and the posterior when samples have been taken.","PeriodicalId":83973,"journal":{"name":"The Statistician : journal of the Institute of Statisticians","volume":"19 1","pages":"475-482"},"PeriodicalIF":0.0,"publicationDate":"2003-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74192358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"50 years of statistics teaching in English schools: some milestones","authors":"Peter Holmes","doi":"10.1046/J.1467-9884.2003.372_1.X","DOIUrl":"https://doi.org/10.1046/J.1467-9884.2003.372_1.X","url":null,"abstract":"Summary. Over the past 50 years the amount of statistics in the English school curriculum has grown from almost nothing to becoming both a major part of the mathematics taught to all 5–16-year-old children and also an integral part of other school subjects. It has also become well established as a General Certificate of Education advanced level subject and as part of mathematics and other subjects at this level. This paper traces the major events that have led to this penetration of the English school curriculum over this period (generally these comments will also be true for Wales and Northern Ireland, but not Scotland) and attempts to draw out some lessons that can be learned to make effective future developments in statistical education.","PeriodicalId":83973,"journal":{"name":"The Statistician : journal of the Institute of Statisticians","volume":"25 1","pages":"439-463"},"PeriodicalIF":0.0,"publicationDate":"2003-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87069779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Sample sizes for designed studies with correlated binary data","authors":"R. Brooks, A. Cottenden, M. Fader","doi":"10.1046/J.1467-9884.2003.00377.X","DOIUrl":"https://doi.org/10.1046/J.1467-9884.2003.00377.X","url":null,"abstract":"The paper considers comparative studies in which subjects use more than one product or receive more than one treatment. The paper is focused mainly on the comparison of products, including the possibility of a large number of products. The data to be analysed are on a binary variable that is observed by each subject for each product used. The calculation of the number of subjects or sample size is illustrated for a variety of basic study designs, assuming that the data may be correlated and the analysis of the data uses generalized estimating equations methodology. The sample sizes use asymptotic theory developed by Liu and Liang. A simulation study that evaluates some of the resulting sample sizes suggests that these need to be increased slightly to achieve the nominal power.","PeriodicalId":83973,"journal":{"name":"The Statistician : journal of the Institute of Statisticians","volume":"26 1","pages":"539-551"},"PeriodicalIF":0.0,"publicationDate":"2003-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77905995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The folded exponential transformation for proportions","authors":"H. Piepho","doi":"10.1046/J.0039-0526.2003.00509.X","DOIUrl":"https://doi.org/10.1046/J.0039-0526.2003.00509.X","url":null,"abstract":"Summary. Several transformations have been suggested for proportions x. Most of these require that 0 < x < 1 and therefore fail when x = 0 or x = 1, which is quite common in practice. The paper proposes a transformation which allows x = 0 and x = 1. It is based on an extension of the exponential transformation and works well for moderately skewed unimodal distributions, whereas J-shaped or very skewed unimodel distributions are less well accommodated. Use of the transformation is exemplified with three data sets.","PeriodicalId":83973,"journal":{"name":"The Statistician : journal of the Institute of Statisticians","volume":"94 1","pages":"575-589"},"PeriodicalIF":0.0,"publicationDate":"2003-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89055007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Analysis of sports data by using bivariate Poisson models","authors":"D. Karlis, I. Ntzoufras","doi":"10.1111/1467-9884.00366","DOIUrl":"https://doi.org/10.1111/1467-9884.00366","url":null,"abstract":"Summary. Models based on the bivariate Poisson distribution are used for modelling sports data. Independent Poisson distributions are usually adopted to model the number of goals of two competing teams. We replace the independence assumption by considering a bivariate Poisson model and its extensions. The models proposed allow for correlation between the two scores, which is a plausible assumption in sports with two opposing teams competing against each other. The effect of introducing even slight correlation is discussed. Using just a bivariate Poisson distribution can improve model fit and prediction of the number of draws in football games. The model is extended by considering an inflation factor for diagonal terms in the bivariate joint distribution. This inflation improves in precision the estimation of draws and, at the same time, allows for overdispersed, relative to the simple Poisson distribution, marginal distributions. The properties of the models proposed as well as interpretation and estimation procedures are provided. An illustration of the models is presented by using data sets from football and water-polo.","PeriodicalId":83973,"journal":{"name":"The Statistician : journal of the Institute of Statisticians","volume":"1 1","pages":"381-393"},"PeriodicalIF":0.0,"publicationDate":"2003-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82255528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Paul A. Smith, M. Pont, Tim Jones, M. Weale, J. Leyland, Gill Eastabrook, P. Allin, N. Longford, Ray Thomas, E. Swires-Hennessy, I. Maclean, D. Holt, Marta Haworth, Peter Kenny, P. Kokic, Peter Lynn
{"title":"Developments in business survey methodology in the Office for National Statistics, 1994-2000. Discussion","authors":"Paul A. Smith, M. Pont, Tim Jones, M. Weale, J. Leyland, Gill Eastabrook, P. Allin, N. Longford, Ray Thomas, E. Swires-Hennessy, I. Maclean, D. Holt, Marta Haworth, Peter Kenny, P. Kokic, Peter Lynn","doi":"10.1111/1467-9884.03571","DOIUrl":"https://doi.org/10.1111/1467-9884.03571","url":null,"abstract":"Since 1994, greater emphasis has been given in the Office for National Statistics to the development of appropriate statistical methodology and its application to business statistics. The paper reviews the main developments in the collection and compilation of business statistics during the period 1994-2000, and the role of the methodologists in these developments. The paper concludes with a look at possible further developments.","PeriodicalId":83973,"journal":{"name":"The Statistician : journal of the Institute of Statisticians","volume":"191 1","pages":"257-295"},"PeriodicalIF":0.0,"publicationDate":"2003-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74885762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Notes on the life and work of R. A. Fisher","authors":"P. Green","doi":"10.1111/1467-9884.00359","DOIUrl":"https://doi.org/10.1111/1467-9884.00359","url":null,"abstract":"","PeriodicalId":83973,"journal":{"name":"The Statistician : journal of the Institute of Statisticians","volume":"17 1","pages":"299-301"},"PeriodicalIF":0.0,"publicationDate":"2003-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84279201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}