Showing papers by "Donald B. Rubin published in 1995"

Bayesian Data Analysis

Abstract: FUNDAMENTALS OF BAYESIAN INFERENCE Probability and Inference Single-Parameter Models Introduction to Multiparameter Models Asymptotics and Connections to Non-Bayesian Approaches Hierarchical Models FUNDAMENTALS OF BAYESIAN DATA ANALYSIS Model Checking Evaluating, Comparing, and Expanding Models Modeling Accounting for Data Collection Decision Analysis ADVANCED COMPUTATION Introduction to Bayesian Computation Basics of Markov Chain Simulation Computationally Efficient Markov Chain Simulation Modal and Distributional Approximations REGRESSION MODELS Introduction to Regression Models Hierarchical Linear Models Generalized Linear Models Models for Robust Inference Models for Missing Data NONLINEAR AND NONPARAMETRIC MODELS Parametric Nonlinear Models Basic Function Models Gaussian Process Models Finite Mixture Models Dirichlet Process Models APPENDICES A: Standard Probability Distributions B: Outline of Proofs of Asymptotic Theorems C: Computation in R and Stan Bibliographic Notes and Exercises appear at the end of each chapter.

Intention-to-treat analysis and the goals of clinical trials

Abstract: Clinical Pharmacology & Therapeutics (1995) 57, 6–15; doi:

In Utero Exposure to Phenobarbital and Intelligence Deficits in Adult Men

Abstract: Objective. —To test whether exposure to phenobarbital in utero is associated with deficits in intelligence scores in adult men and whether the magnitude of the postnatal effect is mediated by exposure parameters and/or postnatal environmental factors. Design. —Two double-blind studies were conducted on independent samples of adult men prenatally exposed to phenobarbital and matched control samples using different measures of general intelligence. Based on data from control subjects, regression models were built relating intelligence scores to relevant pre-exposure matching variables and age at testing. Models generated predicted scores for each exposed subject. Group mean differences between the individually predicted and observed scores estimated exposure effects. Setting. —Copenhagen, Denmark. Participants. —Exposed subjects were adult men born at the largest hospital in Copenhagen between 1959 and 1961 who were exposed to phenobarbital during gestation via maternal medical treatment and whose mothers had no history of a central nervous system disorder and no treatment during pregnancy with any other psychopharmacological drug. Study 1 included 33 men and study 2,81 men. Controls were unexposed members of the same birth cohort matched on a wide spectrum of maternal variables recorded prenatally and perinatally. Controls for studies 1 and 2 included 52 and 101 men, respectively. Main Outcome Measures. —In study 1: Wechsler Adult Intelligence Scale (Danish version); in study 2: Danish Military Draft Board Intelligence Test (Borge Priens Prove). Result. —Men exposed prenatally to phenobarbital had significantly lower verbal intelligence scores (approximately 0.5 SD) than predicted. Lower socioeconomic status and being the offspring of an "unwanted" pregnancy increased the magnitude of the negative effects. Exposure that included the last trimester was the most detrimental. Conclusion. —Phenobarbital exposure during early development can have long-term deleterious effects on cognitive performance. Detrimental environmental conditions can interact with prenatal biological insult to magnify negative outcomes. Physicians are urged to use increased caution in prescribing such medications during pregnancy. (JAMA. 1995;274:1518-1525)

Handling “Don't Know” Survey Responses: The Case of the Slovenian Plebiscite

Abstract: The critical step in the drive toward an independent Slovenia was the plebiscite held in December 1990, at which the citizens of Slovenia voted overwhelmingly in favor of a sovereign and independent state. The Slovenian Public Opinion (SPO) survey of November/December 1990 was used by the government of Slovenia to prepare for the plebiscite. Because the plebiscite counted as “YES voters” only those voters who attended and voted for independence (nonvoters counted as “NO voters”), “Don't Know” survey responses can be thought of as missing data—the true intention of the voter is unknown but must be either “YES” or “NO.” An analysis of the survey data under the missing-at-random assumption for the missing responses provides remarkably accurate estimates of the eventual plebiscite outcome, substantially better than ad hoc methods and a nonignorable model that allows nonresponse to depend on the intended vote.

A method for calibrating false-match rates in record linkage

Abstract: Specifying a record-linkage procedure requires both (1) a method for measuring closeness of agreement between records, typically a scalar weight, and (2) a rule for deciding when to classify records as matches or nonmatches based on the weights. Here we outline a general strategy for the second problem, that is, for accurately estimating false-match rates for each possible cutoff weight. The strategy uses a model where the distribution of observed weights are viewed as a mixture of weights for true matches and weights for false matches. An EM algorithm for fitting mixtures of transformed-normal distributions is used to find posterior modes; associated posterior variability is due to uncertainty about specific normalizing transformations as well as uncertainty in the parameters of the mixture model, the latter being calculated using the SEM algorithm. This mixture-model calibration method is shown to perform well in an applied setting with census data. Further, a simulation experiment reveals that...

Avoiding Model Selection in Bayesian Social Research

Abstract: Raftery's paper addresses two important problems in the statistical analysis of social science data: (1) choosing an appropriate model when so much data are available that standard P-values reject all parsimonious models; and (2) making estimates and predictions when there are not enough data available to fit the desired model using standard techniques. For both problems, we agree with Raftery that classical frequentist methods fail and that Raftery's suggested methods based on BIC can point in better directions. Nevertheless, we disagree with his solutions because, in principle, they are still directed off-target and only by serendipity manage to hit the target in special circumstances. Our primary criticisms of Raftery's proposals are that (1) he promises the impossible: the selection of a model that is adequate for specific purposes without consideration of those purposes; and (2) he uses the same limited tool for model averaging as for model selection, thereby depriving himself of the benefits of the broad range of available Bayesian procedures. Despite our criticisms, we applaud Raftery's desire to improve practice by providing methods and computer programs for all to use and applying these methods to real problems. We believe that his paper makes a positive contribution to social science, by focusing on

The analysis of repeated‐measures data on schizophrenic reaction times using mixture models

Abstract: Reaction times for schizophrenic individuals in a simple visual tracking experiment can be substantially more variable than for non-schizophrenic individuals Current psychological theory suggests that at least some of this extra variability arises from an attentional lapse that delays some, but not all, of each schizophrenic's reaction times Based on this theory, we pursue models in which measurements from non-schizophrenics arise from a normal linear model with a separate mean for each individual, whereas measurements from schizophrenics arise from a mixture of (i) a component analogous to the distribution of response times for non-schizophrenics and (ii) a mean-shifted component We fit four mixture models within this framework, where the distinctions between models arise from assumptions about the variance of the shifted observations and the exchangeability of schizophrenic individuals Some of these models can be fit by maximum likelihood using the EM algorithm, and all can be fit using the ECM algorithm, where the covariance matrices associated with the parameters are calculated by the SEM and SECM algorithms, respectively Bayesian model monitoring using posterior predictive checks is invoked to discard models that fail to reproduce certain observed features of the data and to stimulate the development of better models

Using Mixture Models in Temperament Research

Abstract: Temperamental characteristics can be conceptualised as continuous dimensions or qualitative categories. The continuous versus categorical question concerns the underlying temperamental characteristics and not the measured variables, which can be recorded in either continuous or categorical forms. This paper argues for a categorical conceptualisation of temperamental characteristics and applies a finite mixture model appropriate to this view to two sets of longitudinal observations of infants and young children. This statistical approach provides a good description of the observed predictive relation between behavioural profiles of children at 4 months and the degree of behavioural signs of fear at 14 months. An advantage of the mixture model approach to this data, relative to more standard approaches to developmental data, is that because it takes into account an a-priori theory, it can be used to address improvements and refinements to theories and experimental designs in a straightforward manner.

Bayes, neyman, and calibration

Abstract: The authors (Berk, Western, and Weiss) are to be congratulated for addressing a challenging problem and for providing a variety of comments relevant to it. At the outset, it is important to note that the problem of "apparent populations" is not special to social science. Many fields of science deal with unique datasets, which in no sense are imbedded in randomization-based surveys or experiments, nor which can be viewed as drawn from independent and identically distributed actual replications. Consider cosmology and the origins of the universe, studies of atmospheric pollution, and discussions of the evolution of life on earth (for a fascinating account of William James on precisely this issue of unique datasets, apparent populations, and the evolution of man, see Gould 1988, p. 17). "Could a hypothesized model, with allowance for both its stochastic components and the imperfect estimation of its unknown constants (parameters), have plausibly generated the observed dataset?"-This question is a primitive of science, more fundamental than mathematical statisticians' formalizations of it. Such questions can be legitimately asked of any dataset, whether unique or one of many that was chosen by the scientist according to some randomized design. Addressing such questions with mathematical rigor is more

Beam test of a superconducting cavity for the CESR luminosity upgrade

Abstract: The prototype superconducting cavity system for CESR-Phase III was tested in CESR in August 1994. The performance of the system was very gratifying. The cavity operated gradients of 4.5-6 MV/m and accelerated beam currents up to 220 mA. This current is a factor of 3 above the world record 67 mA for SRF[1]. The high circulating beam current did not increase the heat load or present any danger to the cavity. No instability attributable to the SRF cavity was encountered. A maximum of 155 kW of rf power was transferred to a 120 mA beam. The window was subjected to 125 kW reflected power and processed easily. In the travelling wave mode, vacuum bursts and are trips prevented us from going above 165 kW. The maximum HOM power extracted was 2 kW. Beam stability studies were conducted for a variety of bunch configurations. In other tests a 120 mA beam was bumped horizontally and vertically by /spl plusmn/10 mm. While supporting a 100 mA beam, the cavity was axially deformed with the tuner by 0.4 mm to sweep the HOM frequencies across dangerous revolution harmonics. In all such tests, no resonant excitation of HOMs or beam instabilities were observed, which confirms that the potentially dangerous modes were damped strongly enough to be rendered harmless.