Home  About us  Contact
 

Informative Priors (informative + prior)

Distribution by Scientific Domains
Mathematics and Statistics62%

Terms modified by Informative Priors

  • informative prior distribution
    		•

    Selected Abstracts

    Bayesian Hierarchical Functional Data Analysis Via Contaminated Informative Priors

    BIOMETRICS, Issue 3 2009
    Bruno Scarpa
    Summary A variety of flexible approaches have been proposed for functional data analysis, allowing both the mean curve and the distribution about the mean to be unknown. Such methods are most useful when there is limited prior information. Motivated by applications to modeling of temperature curves in the menstrual cycle, this article proposes a flexible approach for incorporating prior information in semiparametric Bayesian analyses of hierarchical functional data. The proposed approach is based on specifying the distribution of functions as a mixture of a parametric hierarchical model and a nonparametric contamination. The parametric component is chosen based on prior knowledge, while the contamination is characterized as a functional Dirichlet process. In the motivating application, the contamination component allows unanticipated curve shapes in unhealthy menstrual cycles. Methods are developed for posterior computation, and the approach is applied to data from a European fecundability study. [source]

    Dose-Finding with Two Agents in Phase I Oncology Trials

    BIOMETRICS, Issue 3 2003
    Peter F. Thall
    Summary. We propose an adaptive two-stage Bayesian design for finding one or more acceptable dose combinations of two cytotoxic agents used together in a Phase I clinical trial. The method requires that each of the two agents has been studied previously as a single agent, which is almost invariably the case in practice. A parametric model is assumed for the probability of toxicity as a function of the two doses. Informative priors for parameters characterizing the single-agent toxicity probability curves are either elicited from the physician(s) planning the trial or obtained from historical data, and vague priors are assumed for parameters characterizing two-agent interactions. A method for eliciting the single-agent parameter priors is described. The design is applied to a trial of gemcitabine and cyclophosphamide, and a simulation study is presented. [source]

    Bayesian Prediction of Spatial Count Data Using Generalized Linear Mixed Models

    BIOMETRICS, Issue 2 2002
    Ole F. Christensen
    Summary. Spatial weed count data are modeled and predicted using a generalized linear mixed model combined with a Bayesian approach and Markov chain Monte Carlo. Informative priors for a data set with sparse sampling are elicited using a previously collected data set with extensive sampling. Furthermore, we demonstrate that so-called Langevin-Hastings updates are useful for efficient simulation of the posterior distributions, and we discuss computational issues concerning prediction. [source]

    A Probabilistic Framework for Bayesian Adaptive Forecasting of Project Progress

    COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 3 2007
    Paolo Gardoni
    An adaptive Bayesian updating method is used to assess the unknown model parameters based on recorded data and pertinent prior information. Recorded data can include equality, upper bound, and lower bound data. The proposed approach properly accounts for all the prevailing uncertainties, including model errors arising from an inaccurate model form or missing variables, measurement errors, statistical uncertainty, and volitional uncertainty. As an illustration of the proposed approach, the project progress and final time-to-completion of an example project are forecasted. For this illustration construction of civilian nuclear power plants in the United States is considered. This application considers two cases (1) no information is available prior to observing the actual progress data of a specified plant and (2) the construction progress of eight other nuclear power plants is available. The example shows that an informative prior is important to make accurate predictions when only a few records are available. This is also the time when forecasts are most valuable to the project manager. Having or not having prior information does not have any practical effect on the forecast when progress on a significant portion of the project has been recorded. [source]

    Estimation and evidence in forensic anthropology: Sex and race

    AMERICAN JOURNAL OF PHYSICAL ANTHROPOLOGY, Issue 1 2009
    Lyle W. Konigsberg
    Abstract Forensic anthropology typically uses osteological and/or dental data either to estimate characteristics of unidentified individuals or to serve as evidence in cases where there is a putative identification. In the estimation context, the problem is to describe aspects of an individual that may lead to their eventual identification, whereas in the evidentiary context, the problem is to provide the relative support for the identification. In either context, individual characteristics such as sex and race may be useful. Using a previously published forensic case (Steadman et al. (2006) Am J Phys Anthropol 131:15,26) and a large (N = 3,167) reference sample, we show that the sex of the individual can be reliably estimated using a small set of 11 craniometric variables. The likelihood ratio from sex (assuming a 1:1 sex ratio for the "population at large") is, however, relatively uninformative in "making" the identification. Similarly, the known "race" of the individual is relatively uninformative in "making" the identification, because the individual was recovered from an area where the 2000 US census provides a very homogenous picture of (self-identified) race. Of interest in this analysis is the fact that the individual, who was recovered from Eastern Iowa, classifies very clearly with [Howells 1973. Cranial Variation in Man: A Study by Multivariate Analysis of Patterns of Difference Among Recent Human Populations. Cambridge, MA: Peabody Museum of Archaeology and Ethnology; 1989. Skull Shape and the Map: Craniometric Analyses in the Dispersion of Modern Homo. Cambridge, MA: Harvard University Press]. Easter Islander sample in an analysis with uninformative priors. When the Iowa 2000 Census data on self-reported race are used for informative priors, the individual is clearly identified as "American White." This analysis shows the extreme importance of an informative prior in any forensic application. Am J Phys Anthropol 2009. © 2009 Wiley-Liss, Inc. [source]

    Modelling Multivariate Outcomes in Hierarchical Data, with Application to Cluster Randomised Trials

    BIOMETRICAL JOURNAL, Issue 3 2006
    Rebecca M. Turner
    Abstract In the cluster randomised study design, the data collected have a hierarchical structure and often include multivariate outcomes. We present a flexible modelling strategy that permits several normally distributed outcomes to be analysed simultaneously, in which intervention effects as well as individual-level and cluster-level between-outcome correlations are estimated. This is implemented in a Bayesian framework which has several advantages over a classical approach, for example in providing credible intervals for functions of model parameters and in allowing informative priors for the intracluster correlation coefficients. In order to declare such informative prior distributions, and fit models in which the between-outcome covariance matrices are constrained, priors on parameters within the covariance matrices are required. Careful specification is necessary however, in order to maintain non-negative definiteness and symmetry between the different outcomes. We propose a novel solution in the case of three multivariate outcomes, and present a modified existing approach and novel alternative for four or more outcomes. The methods are applied to an example of a cluster randomised trial in the prevention of coronary heart disease. The modelling strategy presented would also be useful in other situations involving hierarchical multivariate outcomes. (© 2006 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source]

    Bayesian Modeling of Age-Specific Survival in Bird Nesting Studies under Irregular Visits

    BIOMETRICS, Issue 4 2003
    Chong Z. He
    Summary. In this article, a Bayesian model for age-specific nest survival rates is presented to handle the irregular visit case. Both informative priors and noninformative priors are investigated. The reference prior under this model is derived, and, therefore, the hyperparameter specification problem is solved to some extent. The Bayesian method provides a more accurate estimate of the total survival rate than the standard Mayfield method, if the age-specific hazard rates are not constant. The Bayesian method also lets the biologist look for high- and low-survival rates during the whole nesting period. In practice, it is common for data of several types to be collected in a single study. That is, some nests may be aged, others are not. Some nests are visited regularly; others are visited irregularly. The Bayesian method accommodates any mix of these sampling techniques by assuming that the aging and visiting activities have no effect on the survival rate. The methods are illustrated by an analysis of the Missouri northern bobwhite data set. [source]