bluuebunny/crossref_metadata_2025_split huggingface dataset:
Detection of Interactions in Experiments on Large Numbers of Factors
Authors: Lewis S. M. , Dean A. M. 
Published: March 2003

Summary
One of the main advantages of factorial experiments is the information that they can offer on interactions. When there are many factors to be studied, some or all of this information is often sacrificed to keep the size of an experiment economically feasible. Two strategies for group screening are presented for a large number of factors, over two stages of experimentation, with particular emphasis on the detection of interactions. One approach estimates only main effects at the first stage (classical group screening), whereas the other new method (interaction group screening) estimates both main effects and key two-factor interactions at the first stage. Three criteria are used to guide the choice of screening technique, and also the size of the groups of factors for study in the first-stage experiment. The criteria seek to minimize the expected total number of observations in the experiment, the probability that the size of the experiment exceeds a prespecified target and the proportion of active individual factorial effects which are not detected. To implement these criteria, results are derived on the relationship between the grouped and individual factorial effects, and the probability distributions of the numbers of grouped factors whose main effects or interactions are declared active at the first stage. Examples are used to illustrate the methodology, and some issues and open questions for the practical implementation of the results are discussed.
Model Selection in Spline Nonparametric Regression
Authors: Wood Sally , Kohn Robert , Shively Tom , Jiang Wenxin 
Published: March 2003

SummaryA Bayesian approach is presented for model selection in nonparametric regression with Gaussian errors and in binary nonparametric regression. A smoothness prior is assumed for each component of the model and the posterior probabilities of the candidate models are approximated using the Bayesian information criterion. We study the model selection method by simulation and show that it has excellent frequentist properties and gives improved estimates of the regression surface. All the computations are carried out efficiently using the Gibbs sampler.
Models for Survival Data from Cancer Prevention Studies
Authors: Sinha Debajyoti , Ibrahim Joseph G. , Chen Ming-Hui 
Published: March 2003

SummaryWe propose a new parametric survival model for cancer prevention studies. The formulation of the model is in the spirit of stochastic modelling of the occurrences of tumours through two stages: initiation of an undetected tumour and promotion of the tumour to a detectable cancer. Several novel properties of the model proposed are derived. In addition, we examine the relationship of our model with the existing lagged regression model of Zucker and Lakatos. Also, we bridge the difference between two distinct stochastic modelling methods for cancer data, one used primarily for cancer therapeutic trials and the other used for cancer prevention trials.
Operating Characteristics and Extensions of the False Discovery Rate Procedure
Authors: Genovese Christopher , Wasserman Larry 
Published: March 2003

SummaryWe investigate the operating characteristics of the Benjamini–Hochberg false discovery rate procedure for multiple testing. This is a distribution-free method that controls the expected fraction of falsely rejected null hypotheses among those rejected. The paper provides a framework for understanding more about this procedure. We first study the asymptotic properties of the ‘deciding point' D that determines the critical p-value. From this, we obtain explicit asymptotic expressions for a particular risk function. We introduce the dual notion of false non-rejections and we consider a risk function that combines the false discovery rate and false non-rejections. We also consider the optimal procedure with respect to a measure of conditional risk.
Bayes Model Averaging with Selection of Regressors
Authors: Brown P. J. , Vannucci M. , Fearn T. 
Published: March 2003

SummaryWhen a number of distinct models contend for use in prediction, the choice of a single model can offer rather unstable predictions. In regression, stochastic search variable selection with Bayesian model averaging offers a cure for this robustness issue but at the expense of requiring very many predictors. Here we look at Bayes model averaging incorporating variable selection for prediction. This offers similar mean-square errors of prediction but with a vastly reduced predictor space. This can greatly aid the interpretation of the model. It also reduces the cost if measured variables have costs. The development here uses decision theory in the context of the multivariate general linear model. In passing, this reduced predictor space Bayes model averaging is contrasted with single-model approximations. A fast algorithm for updating regressions in the Markov chain Monte Carlo searches for posterior inference is developed, allowing many more variables than observations to be contemplated. We discuss the merits of absolute rather than proportionate shrinkage in regression, especially when there are more variables than observations. The methodology is illustrated on a set of spectroscopic data used for measuring the amounts of different sugars in an aqueous solution.
Modelling Spatially Correlated Data via Mixtures: A Bayesian Approach
Authors: Fernández Carmen , Green Peter J. 
Published: March 2003

SummaryThe paper develops mixture models for spatially indexed data. We confine attention to the case of finite, typically irregular, patterns of points or regions with prescribed spatial relationships, and to problems where it is only the weights in the mixture that vary from one location to another. Our specific focus is on Poisson-distributed data, and applications in disease mapping. We work in a Bayesian framework, with the Poisson parameters drawn from gamma priors, and an unknown number of components. We propose two alternative models for spatially dependent weights, based on transformations of autoregressive Gaussian processes: in one (the logistic normal model), the mixture component labels are exchangeable; in the other (the grouped continuous model), they are ordered. Reversible jump Markov chain Monte Carlo algorithms for posterior inference are developed. Finally, the performances of both of these formulations are examined on synthetic data and real data on mortality from a rare disease.
Least Squares Variogram Fitting by Spatial Subsampling
Authors: Lee Yoon Dong , Lahiri Soumendra N. 
Published: March 2003

SummaryLeast squares methods are popular for fitting valid variogram models to spatial data. The paper proposes a new least squares method based on spatial subsampling for variogram model fitting. We show that the method proposed is statistically efficient among a class of least squares methods, including the generalized least squares method. Further, it is computationally much simpler than the generalized least squares method. The method produces valid variogram estimators under very mild regularity conditions on the underlying random field and may be applied with different choices of the generic variogram estimator without analytical calculation. An extension of the method proposed to a class of spatial regression models is illustrated with a real data example. Results from a simulation study on finite sample properties of the method are also reported.
Pattern–Mixture and Selection Models for Analysing Longitudinal Data with Monotone Missing Patterns
Authors: Birmingham Jolene , Rotnitzky Andrea , Fitzmaurice Garrett M. 
Published: March 2003

SummaryWe examine three pattern–mixture models for making inference about parameters of the distribution of an outcome of interest Y that is to be measured at the end of a longitudinal study when this outcome is missing in some subjects. We show that these pattern–mixture models also have an interpretation as selection models. Because these models make unverifiable assumptions, we recommend that inference about the distribution of Y be repeated under a range of plausible assumptions. We argue that, of the three models considered, only one admits a parameterization that facilitates the examination of departures from the assumption of sequential ignorability. The three models are nonparametric in the sense that they do not impose restrictions on the class of observed data distributions. Owing to the curse of dimensionality, the assumptions that are encoded in these models are sufficient for identification but not for inference. We describe additional flexible and easily interpretable assumptions under which it is possible to construct estimators that are well behaved with moderate sample sizes. These assumptions define semiparametric models for the distribution of the observed data. We describe a class of estimators which, up to asymptotic equivalence, comprise all the consistent and asymptotically normal estimators of the parameters of interest under the postulated semiparametric models. We illustrate our methods with the analysis of data from a randomized clinical trial of contracepting women.
Markov Chain Monte Carlo Methods for Fitting Spatiotemporal Stochastic Models in Plant Epidemiology
Authors: Gibson Gavin J. 
Published: March 2003

SUMMARY
Strategies for controlling plant epidemics are investigated by fitting continuous time spatiotemporal stochastic models to data consisting of maps of disease incidence observed at discrete times. Markov chain Monte Carlo methods are used for fitting two such models to data describing the spread of citrus tristeza virus (CTV) in an orchard. The approach overcomes some of the difficulties encountered when fitting stochastic models to infrequent observations of a continuous process. The results of the analysis cast doubt on the effectiveness of a strategy identified from a previous spatial analysis of the CTV data. Extensions of the approaches to more general models and other problems are also considered.
Scaling unobservable constructs in social science
Authors: Bartholomew D. J. 
Published: March 2003

SUMMARY
The use of the logit–probit model for constructing social measurement scales is explored by using two data sets. The first was specifically designed for scaling and consists of answers by 1490 individuals to five questions on economic self-determination. The second is from the Workplace Industrial Relations Survey and relates to answers to six questions addressed to 1005 firms about innovations. It is shown that although scales can be constructed their precision (reliability) is likely to be low. Further, for examples like these, latent class models may give an equally satisfactory account of the data.
The Muscatine Children’s Obesity Data Reanalysed Using Pattern Mixture Models
Authors: Ekholm Anders , Skinner Chris 
Published: March 2003

SUMMARY
A set of longitudinal binary, partially incomplete, data on obesity among children in the USA is reanalysed. The multivariate Bernoulli distribution is parameterized by the univariate marginal probabilities and dependence ratios of all orders, which together support maximum likelihood inference. The temporal association of obesity is strong and complex but stationary. We fit a saturated model for the distribution of response patterns and find that non-response is missing completely at random for boys but that the probability of obesity is consistently higher among girls who provided incomplete records than among girls who provided complete records. We discuss the statistical and substantive features of, respectively, pattern mixture and selection models for this data set.
Extremal Analysis of Short Series with Outliers: Sea-Levels and Athletics Records
Authors: Barão M. Isabel , Tawn Jonathan. A. 
Published: March 2003

SUMMARY
The analysis of extreme values is often required from short series which are biasedly sampled or contain outliers. Data for sea-levels at two UK east coast sites and data on athletics records for women’s 3000 m track races are shown to exhibit such characteristics. Univariate extreme value methods provide a poor quantification of the extreme values for these data. By using bivariate extreme value methods we analyse jointly these data with related observations, from neighbouring coastal sites and 1500 m races respectively. We show that using bivariate methods provides substantial benefits, both in these applications and more generally with the amount of information gained being determined by the degree of dependence, the lengths and the amount of overlap of the two series, the homogeneity of the marginal characteristics of the variables and the presence and type of the outlier.
Analysing Drug Abuse with British Crime Survey Data: Modelling and Questionnaire Design Issues
Authors: MacDonald Ziggy , Pudney Stephen 
Published: March 2003

SUMMARY
We use the British Crime Survey (BCS) to analyse the demand for illicit drugs and the implications of drug use for the probability of subsequent unemployment. We demonstrate that the BCS questionnaire has a serious design flaw for this purpose, and we propose some simple modifications. We also develop a modelling technique that is suitable for existing BCS data and apply it to the 1994 and 1996 samples. We find evidence that soft drug use is associated with a greatly increased probability of later hard drug use and that past drug use is associated with increased probabilities of unemployment.
Space–Time Calibration of Radar Rainfall Data
Authors: Brown Patrick E. , Diggle Peter J. , Lord Martin E. , Young Peter C. 
Published: March 2003

SUMMARY
Motivated by a specific problem concerning the relationship between radar reflectance and rainfall intensity, the paper develops a space–time model for use in environmental monitoring applications. The model is cast as a high dimensional multivariate state space time series model, in which the cross-covariance structure is derived from the spatial context of the component series, in such a way that its interpretation is essentially independent of the particular set of spatial locations at which the data are recorded. We develop algorithms for estimating the parameters of the model by maximum likelihood, and for making spatial predictions of the radar calibration parameters by using realtime computations. We apply the model to data from a weather radar station in Lancashire, England, and demonstrate through empirical validation the predictive performance of the model.
Joint Analysis of Longitudinal Data Comprising Repeated Measures and Times to Events
Authors: Xu Jane , Zeger Scott L. 
Published: March 2003

SUMMARY
In biomedical and public health research, both repeated measures of biomarkers Y as well as times T to key clinical events are often collected for a subject. The scientific question is how the distribution of the responses [T, Y|X] changes with covariates X. [T|X] may be the focus of the estimation where Y can be used as a surrogate for T. Alternatively, T may be the time to drop-out in a study in which [Y|X] is the target for estimation. Also, the focus of a study might be on the effects of covariates X on both T and Y or on some underlying latent variable which is thought to be manifested in the observable outcomes. In this paper, we present a general model for the joint analysis of [T, Y|X] and apply the model to estimate [T|X] and other related functionals by using the relevant information in both T and Y. We adopt a latent variable formulation like that of Fawcett and Thomas and use it to estimate several quantities of clinical relevance to determine the efficacy of a treatment in a clinical trial setting. We use a Markov chain Monte Carlo algorithm to estimate the model’s parameters. We illustrate the methodology with an analysis of data from a clinical trial comparing risperidone with a placebo for the treatment of schizophrenia.
Denoising Radiocommunications Signals by Using Iterative Wavelet Shrinkage
Authors: Baxter Paul D. , Upton Graham J. G. 
Published: March 2003

SummaryRadiocommunications signals pose particular problems in the context of statistical signal processing. This is because short-term fluctuations (noise) are a consequence of atmospheric effects whose characteristics vary in both the short and the longer term. We contrast traditional time domain and frequency domain filters with wavelet methods. We also propose an iterative wavelet procedure which appears to provide benefits over existing wavelet methods.
Effect Modification in a Randomized Trial under Non-Ignorable Non-Compliance: An Application to the Alpha-Tocopherol Beta-Carotene Study
Authors: Korhonen Pasi , Palmgren Juni 
Published: March 2003

Summary
We propose an inference procedure that allows an estimation of the effects of treatment modifying factors on a survival end point in a placebo-controlled randomized trial with non-ignorable non-compliance. Against prior expectation the alpha-tocopherol beta-carotene (ATBC) study showed an increase in the incidence of lung cancer and total mortality following supplementation with beta-carotene. Any attempt to assess interactions between beta-carotene supplementation and base-line covariates needs to address the issue of non-compliance. In the ATBC study about 25% of the participants dropped out from the active follow-up prematurely for reasons other than death and they stopped receiving the study supplementation. Survival status was observed regardless of drop-out for all participants. Drop-out is related to base-line covariates, to the treatment arm and presumably also to the health status of the individuals through both measured and unmeasured factors. Non-ignorable non-compliance is induced by unmeasured covariates related both to drop-out from active participation in the study and to health status and death. Instead of comparing survival between the randomized groups we focus on an estimation of the effect of beta-carotene actually received and its interaction with base-line covariates such as age, smoking and alcohol consumption. We use an accelerated failure time model to link a potential treatment-free survival time to the actual observed survival time and to the treatment received and its interactions with base-line covariates. We refer to this as the structural model. The estimation of the parameters in the structural model is based on minimizing a score test statistic obtained from the partial likelihood specified for the treatment-free survival time under the structural model. We compare the method proposed with Robins's g-estimation based on a linear rank test statistic. If the structural model holds, then the method proposed can substantially increase efficiency. We propose diagnostic techniques for evaluating the adequacy of the structural model and discuss how a misspecification of the model may induce bias into the score equations.
Latent Class Models for Longitudinal Studies of the Elderly with Data Missing at Random
Authors: Reboussin Beth A , Miller Michael E , Lohman Kurt K , Ten Have Thomas R 
Published: March 2003

Summary
The elderly population in the USA is expected to double in size by the year 2025, making longitudinal health studies of this population of increasing importance. The degree of loss to follow-up in studies of the elderly, which is often because elderly people cannot remain in the study, enter a nursing home or die, make longitudinal studies of this population problematic. We propose a latent class model for analysing multiple longitudinal binary health outcomes with multiple-cause non-response when the data are missing at random and a non-likelihood-based analysis is performed. We extend the estimating equations approach of Robins and co-workers to latent class models by reweighting the multiple binary longitudinal outcomes by the inverse probability of being observed. This results in consistent parameter estimates when the probability of non-response depends on observed outcomes and covariates (missing at random) assuming that the model for non-response is correctly specified. We extend the non-response model so that institutionalization, death and missingness due to failure to locate, refusal or incomplete data each have their own set of non-response probabilities. Robust variance estimates are derived which account for the use of a possibly misspecified covariance matrix, estimation of missing data weights and estimation of latent class measurement parameters. This approach is then applied to a study of lower body function among a subsample of the elderly participating in the 6-year Longitudinal Study of Aging.
Put Option Premiums and Coherent Risk Measures
Authors: Jarrow Robert 
Published: March 2003

This note defines the premium of a put option on the firm as a measure of insolvency risk. The put premium is not a coherent risk measure as defined by Artzner et al. (1999). It satisfies all the axioms for a coherent risk measure except one, the translation invariance axiom. However, it satisfies a weakened version of the translation invariance axiom that we label translation monotonicity. The put premium risk measure generates an acceptance set that satisfies the regularity Axioms 2.1–2.4 of Artzner et al. (1999). In fact, this is a general result for any risk measure satisfying the same risk measure axioms as the put premium. Finally, the coherent risk measure generated by the put premium's acceptance set is the minimal capital required to protect the firm against insolvency uniformly across all states of nature.
Bargaining Health Benefits in the Workplace: An Inside View
Authors: Montagne C. 
Published: March 2003

In early 1999, as a professor of health economics and a union representative at a major university, I served on a health benefits bargaining committee, preparing for formal contract negotiations to begin that summer. The committee had an “issue‐based” bargaining format and consisted of faculty union representatives and members of the university administration. They were asked to submit a report that the bargainers could use in the formal negotiations. Both the union representatives and the members of the university administration hoped that the committee could prepare the bargainers well enough so that they would not have to settle health care issues using last‐minute compromises and ad hoc formulas. This article examines the process of writing this report and its results.Negotiating health care coverage in the workplace is a fundamental issue in health care reform in the United States. Largely because of historical factors, most Americans receive health benefits through their (or their spouse's) employer, benefits that are provided and bargained as part of a larger wage and salary package.Before contract negotiations in 1999, the author served on an "issue‐based" health benefits committee of faculty union representatives and university administrators. Although the committee solicited estimates from health insurers regarding the impact of higher copayments on monthly premiums, in subsequent negotiations, the projected cost savings did not lead to changes in coverage or copayments. The explanations offered are (1) national or regional employers may be reluctant to raise employees' health benefit copayments when labor markets are tight; (2) collective bargaining, particularly when other, nonmonetary issues are being bargained, may lead to results different from those from a strictly competitive model; and (3) employers with market power in the product market may shift these higher costs to consumers through higher prices.