{"title":"Eliciting Gaussian process priors for complex computer codes","authors":"J. Oakley","doi":"10.1111/1467-9884.00300","DOIUrl":"https://doi.org/10.1111/1467-9884.00300","url":null,"abstract":"Summary. We consider the problem of eliciting expert knowledge about the output of a deterministic computer code, where the output is a function of a vector of input variables. A Gaussian process prior is assumed for the unknown function, and expert judgments about the output at various inputs are used to find suitable hyperparameters of the Gaussian process prior distribution. An example is presented involving the movement of radionuclides in the food chain.","PeriodicalId":83973,"journal":{"name":"The Statistician : journal of the Institute of Statisticians","volume":"134 1","pages":"81-97"},"PeriodicalIF":0.0,"publicationDate":"2002-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75069803","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":"A New Look at Models For Exponential Smoothing","authors":"C. Chatfield, A. Koehler, J. Ord, R. Snyder","doi":"10.1111/1467-9884.00267","DOIUrl":"https://doi.org/10.1111/1467-9884.00267","url":null,"abstract":"Exponential smoothing (ES) forecasting methods are widely used but are often dis-cussed without recourse to a formal statistical framework. This paper reviews and compares a variety of potential models for ES. As well as autoregressive integrated moving average and structural models, a promising class of dynamic non-linear state space models is described that allows for a changing variance. The richness of possible models helps to explain why ES methods seem to be robust in practice. A modelling approach can enhance the forecaster's ability to identify pertinent components of time series variation, and to obtain more reliable estimates of prediction error variances. The paper should be of particular interest to those engaged in forecasting appli-cations where strategies that allow for risk and uncertainty are needed.","PeriodicalId":83973,"journal":{"name":"The Statistician : journal of the Institute of Statisticians","volume":"150 1","pages":"147-159"},"PeriodicalIF":0.0,"publicationDate":"2001-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77994227","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":"New table-tennis rating system","authors":"David J. Marcus","doi":"10.1111/1467-9884.00271","DOIUrl":"https://doi.org/10.1111/1467-9884.00271","url":null,"abstract":"Summary. We present a new system for rating the playing strength of table-tennis players. The system is based on Bayesian principles and is designed to handle a large changing population of players, where some players play frequently whereas other players play infrequently. The system takes into account the length of time since a player last played a tournament. When processing matches in a single tournament, the system takes into account how a player’s opponents did in the same tournament. The system has been tested by processing data from 5½ years of tournaments (15549 players and 330079 matches). The system could be adapted to other sports that involve head-to-head competition.","PeriodicalId":83973,"journal":{"name":"The Statistician : journal of the Institute of Statisticians","volume":"1 1","pages":"191-208"},"PeriodicalIF":0.0,"publicationDate":"2001-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73311124","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 advent of the sample survey in the social sciences","authors":"L. Brunt","doi":"10.1111/1467-9884.00270","DOIUrl":"https://doi.org/10.1111/1467-9884.00270","url":null,"abstract":"It is generally thought that the sample survey was developed in the social sciences in the late 19th century. We trace its inception back to the survey of rural economy that was undertaken by Arthur Young in England in 1768. Young was aware that his survey technique was an important innovation, and he discussed the best method of data collection to ensure that the sample was representative of the population. His contemporaries, such as Davies and Eden, recognized the potential of sample surveys and soon undertook important investigations based on Young's model. Sample surveys declined in importance during the 19th century as census data became more abundant and mathematicians formalized the theory of population statistics.","PeriodicalId":83973,"journal":{"name":"The Statistician : journal of the Institute of Statisticians","volume":"66 1","pages":"179-189"},"PeriodicalIF":0.0,"publicationDate":"2001-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78447475","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":"Effects of adjustment errors on discrete feed-back dead band control schemes","authors":"A. Luceño","doi":"10.1111/1467-9884.00269","DOIUrl":"https://doi.org/10.1111/1467-9884.00269","url":null,"abstract":"This paper analyses the influence of random adjustment errors on the average adjustment interval and mean-squared deviation from the target corresponding to discrete feed-back dead band control schemes. The study is performed in a general setting in which there may be measurement and adjustment errors and deterministic process drift. The possibility that the costs of taking an observation, of making an adjustment and of being off target may be important is also considered.","PeriodicalId":83973,"journal":{"name":"The Statistician : journal of the Institute of Statisticians","volume":"9 1","pages":"169-177"},"PeriodicalIF":0.0,"publicationDate":"2001-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78466194","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":"Statistical Inferences For Interobserver Agreement Studies With Nominal Outcome Data","authors":"E. Bartfay, A. Donner","doi":"10.1111/1467-9884.00266","DOIUrl":"https://doi.org/10.1111/1467-9884.00266","url":null,"abstract":"Most statistical methods for interobserver agreement studies involving categorical data focus on dichotomous outcome variables, whereas only a limited number of methods have been developed for nominal outcome data. As a consequence, researchers may resort to dichotomization simply to facilitate the analysis of data, hence discarding potentially valuable information. We present three inference procedures for hypothesis testing concerning interobserver agreement studies with nominal data. These procedures may be applied to studies characterized by two observers and three or more outcome categories. We illustrate these methods by using previously published data sets.","PeriodicalId":83973,"journal":{"name":"The Statistician : journal of the Institute of Statisticians","volume":"10 3","pages":"135-146"},"PeriodicalIF":0.0,"publicationDate":"2001-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72588244","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 Moments of Ratios are Ratios of Moments","authors":"D. Conniffe, J. E. Spencer","doi":"10.1111/1467-9884.00268","DOIUrl":"https://doi.org/10.1111/1467-9884.00268","url":null,"abstract":"Finding the moments of a ratio can be very difficult, but if the ratio is independent of its own denominator the solution simplifies to the moments of the numerator divided by those of the denominator. Such an independence situation occurs surprisingly frequently in important statistical problems. However, its occurrence has not always been noticed and, consequently, the device of obtaining moments of ratios as ratios of moments has often not been employed when it should have. The history of the device is one of discovery, inadequate dissemination leading to neglect and rediscovery, with the eventual users often unaware of the true originators. This paper charts that history and discusses the applications of the device, including some of recent vintage.","PeriodicalId":83973,"journal":{"name":"The Statistician : journal of the Institute of Statisticians","volume":"8 1","pages":"161-168"},"PeriodicalIF":0.0,"publicationDate":"2001-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80276767","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":"Deciding How Long to Test Software","authors":"Kevin McDaid, Simon P. Wilson","doi":"10.1111/1467-9884.00265","DOIUrl":"https://doi.org/10.1111/1467-9884.00265","url":null,"abstract":"Testing software before its release is an important stage of the software testing process. We propose a decision theoretic solution to the problem of deciding the optimal length of the testing period. We make use of a well-known error detection model and a sensible utility. Three testing plans are described: the single stage, the two stage and the next fixed time look ahead. A study to compare the performance of the plans shows the relative performance of each plan under a variety of assumptions about the quality of the software to be tested. All the plans are illustrated by using the well-known naval tactical data system data set.","PeriodicalId":83973,"journal":{"name":"The Statistician : journal of the Institute of Statisticians","volume":"55 1","pages":"117-134"},"PeriodicalIF":0.0,"publicationDate":"2001-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72769765","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":"Modelling Rental Guide Data Using Mean and Dispersion Additive Models","authors":"D. Stasinopoulos, R. Rigby, L. Fahrmeir","doi":"10.1111/1467-9884.00247","DOIUrl":"https://doi.org/10.1111/1467-9884.00247","url":null,"abstract":"Summary. This paper analyses rent data obtained from a survey conducted for the 1994 rental guide for the city of Munich. We describe how mean and dispersion additive models can be applied to the situation where both the mean and the heterogeneity in the dependent variable, rent here, need to be modelled by using parametric and/or nonparametric functions of explanatory variables. The skewness in the rent variable is dealt with either by using a Box-Cox transformation to normality or by modelling the rent variable by using a family of skew distributions. A gamma distribution model was chosen to provide centiles estimates and intervals for the rent variable.","PeriodicalId":83973,"journal":{"name":"The Statistician : journal of the Institute of Statisticians","volume":"455 1","pages":"479-493"},"PeriodicalIF":0.0,"publicationDate":"2000-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83001310","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 model selection approach to analysis of variance under heteroscedasticity","authors":"F. Bertolino, W. Racugno, E. Moreno","doi":"10.1111/1467-9884.00249","DOIUrl":"https://doi.org/10.1111/1467-9884.00249","url":null,"abstract":"The classical Bayesian approach to analysis of variance assumes the homoscedastic condition and uses conventional uniform priors on the location parameters and on the logarithm of the common scale. The problem has been developed as one of estimation of location parameters. We argue that this does not lead to an appropriate Bayesian solution. A solution based on a Bayesian model selection procedure is proposed. Our development is in the general heteroscedastic setting in which a frequentist exact test does not exist. The Bayes factor involved uses intrinsic and fractional priors which are used instead of the usual default prior distributions for which the Bayes factor is not well defined. The behaviour of these Bayes factors is compared with the Bayesian information criterion of Schwarz and the frequentist asymptotic approximations of Welch and Brown and Forsythe.","PeriodicalId":83973,"journal":{"name":"The Statistician : journal of the Institute of Statisticians","volume":"44 1","pages":"495-502"},"PeriodicalIF":0.0,"publicationDate":"2000-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87538695","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}