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Statistical Literature (statistical + literature)
Selected AbstractsA new rank correlation coefficient with application to the consensus ranking problemJOURNAL OF MULTI CRITERIA DECISION ANALYSIS, Issue 1 2002Edward J. Emond Abstract The consensus ranking problem has received much attention in the statistical literature. Given m rankings of n objects the objective is to determine a consensus ranking. The input rankings may contain ties, be incomplete, and may be weighted. Two solution concepts are discussed, the first maximizing the average weighted rank correlation of the solution ranking with the input rankings and the second minimizing the average weighted Kemeny,Snell distance. A new rank correlation coefficient called ,x is presented which is shown to be the unique rank correlation coefficient which is equivalent to the Kemeny-Snell distance metric. The new rank correlation coefficient is closely related to Kendall's tau but differs from it in the way ties are handled. It will be demonstrated that Kendall's ,b is flawed as a measure of agreement between weak orderings and should no longer be used as a rank correlation coefficient. The use of ,x in the consensus ranking problem provides a more mathematically tractable solution than the Kemeny,Snell distance metric because all the ranking information can be summarized in a single matrix. The methods described in this paper allow analysts to accommodate the fully general consensus ranking problem with weights, ties, and partial inputs. Copyright © 2002 John Wiley & Sons, Ltd. [source] The ,heuristics and biases' bias in expert elicitationJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES A (STATISTICS IN SOCIETY), Issue 1 2008Mary Kynn Summary., In the early 1970s Tversky and Kahneman published a series of papers on ,heuristics and biases' describing human inadequacies in assessing probabilities, culminating in a highly popular article in Science. This seminal research has been heavily cited in many fields, including statistics, as the definitive research on probability assessment. Curiously, although this work was debated at the time and more recent work has largely refuted many of the claims, this apparent heuristics and biases bias in elicitation research has gone unremarked. Over a decade of research into the frequency effect, the importance of framing, and cognitive models more generally, has been almost completely ignored by the statistical literature on expert elicitation. To remedy this situation, this review offers a guide to the psychological research on assessing probabilities, both old and new, and gives concrete guidelines for eliciting expert knowledge. [source] Analysis of longitudinal data with drop-out: objectives, assumptions and a proposalJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES C (APPLIED STATISTICS), Issue 5 2007Peter Diggle Summary. The problem of analysing longitudinal data that are complicated by possibly informative drop-out has received considerable attention in the statistical literature. Most researchers have concentrated on either methodology or application, but we begin this paper by arguing that more attention could be given to study objectives and to the relevant targets for inference. Next we summarize a variety of approaches that have been suggested for dealing with drop-out. A long-standing concern in this subject area is that all methods require untestable assumptions. We discuss circumstances in which we are willing to make such assumptions and we propose a new and computationally efficient modelling and analysis procedure for these situations. We assume a dynamic linear model for the expected increments of a constructed variable, under which subject-specific random effects follow a martingale process in the absence of drop-out. Informal diagnostic procedures to assess the tenability of the assumption are proposed. The paper is completed by simulations and a comparison of our method and several alternatives in the analysis of data from a trial into the treatment of schizophrenia, in which approximately 50% of recruited subjects dropped out before the final scheduled measurement time. [source] A New Method for Constructing Confidence Intervals for the Index CpmQUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 7 2004Michael Perakis Abstract In the statistical literature on the study of the capability of processes through the use of indices, Cpm appears to have been one of the most widely used capability indices and its estimation has attracted much interest. In this article, a new method for constructing approximate confidence intervals or lower confidence limits for this index is suggested. The method is based on an approximation of the non-central chi-square distribution, which was proposed by Pearson. Its coverage appears to be more satisfactory compared with that achieved by any of the two most widely used methods that were proposed by Boyles, in situations where one is interested in assessing a lower confidence limit for Cpm. This is supported by the results of an extensive simulation study. Copyright © 2004 John Wiley & Sons, Ltd. [source] Multiple-Imputation-Based Residuals and Diagnostic Plots for Joint Models of Longitudinal and Survival OutcomesBIOMETRICS, Issue 1 2010Dimitris Rizopoulos Summary The majority of the statistical literature for the joint modeling of longitudinal and time-to-event data has focused on the development of models that aim at capturing specific aspects of the motivating case studies. However, little attention has been given to the development of diagnostic and model-assessment tools. The main difficulty in using standard model diagnostics in joint models is the nonrandom dropout in the longitudinal outcome caused by the occurrence of events. In particular, the reference distribution of statistics, such as the residuals, in missing data settings is not directly available and complex calculations are required to derive it. In this article, we propose a multiple-imputation-based approach for creating multiple versions of the completed data set under the assumed joint model. Residuals and diagnostic plots for the complete data model can then be calculated based on these imputed data sets. Our proposals are exemplified using two real data sets. [source] Latent Pattern Mixture Models for Informative Intermittent Missing Data in Longitudinal StudiesBIOMETRICS, Issue 2 2004Haiqun Lin Summary. A frequently encountered problem in longitudinal studies is data that are missing due to missed visits or dropouts. In the statistical literature, interest has primarily focused on monotone missing data (dropout) with much less work on intermittent missing data in which a subject may return after one or more missed visits. Intermittent missing data have broader applicability that can include the frequent situation in which subjects do not have common sets of visit times or they visit at nonprescheduled times. In this article, we propose a latent pattern mixture model (LPMM), where the mixture patterns are formed from latent classes that link the longitudinal response and the missingness process. This allows us to handle arbitrary patterns of missing data embodied by subjects' visit process, and avoids the need to specify the mixture patterns a priori. One assumption of our model is that the missingness process is assumed to be conditionally independent of the longitudinal outcomes given the latent classes. We propose a noniterative approach to assess this key assumption. The LPMM is illustrated with a data set from a health service research study in which homeless people with mental illness were randomized to three different service packages and measures of homelessness were recorded at multiple time points. Our model suggests the presence of four latent classes linking subject visit patterns to homeless outcomes. [source] | ||||||||||