Home About us Contact
|
Home About us Contact | ||
|
|
||
Chain Monte Carlo (chain + monte_carlo)
Kinds of Chain Monte Carlo Selected AbstractsTK/TD dose,response modeling of toxicityENVIRONMETRICS, Issue 5 2007Munni Begum Abstract In environmental cancer risk assessment of a toxic chemical, the main focus is in understanding induced target organ toxicity that may in turn lead to carcinogenicity. Mathematical models based on systems of ordinary differential equations with biologically relevant parameters are tenable methods for describing the disposition of chemicals in target organs. In evaluation of a toxic chemical, dose,response assessment often addresses only toxicodynamics (TD) of the chemical, while its toxicokinetics (TK) do not enter into consideration. The primary objective of this research is to integrate both TK and TD in evaluation of toxic chemicals while performing dose,response assessment. Population models, with hierarchical setup and nonlinear predictors, for TK concentration and TD effect measures are considered. A one-compartment model with biologically relevant parameters, such as organ volume, uptake rate and excretion rate, or clearance, is used to derive the TK predictor while a two parameter Emax model is used as a predictor for TD measures. Inference of the model parameters with nonnegative and assay's Limit of Detection (LOD) constraints was carried out by Bayesian approaches using Markov Chain Monte Carlo (MCMC) techniques. Copyright © 2006 John Wiley & Sons, Ltd. [source] Testing Conditional Asset Pricing Models Using a Markov Chain Monte Carlo ApproachEUROPEAN FINANCIAL MANAGEMENT, Issue 3 2008Manuel Ammann G12 Abstract We use Markov Chain Monte Carlo (MCMC) methods for the parameter estimation and the testing of conditional asset pricing models. In contrast to traditional approaches, it is truly conditional because the assumption that time variation in betas is driven by a set of conditioning variables is not necessary. Moreover, the approach has exact finite sample properties and accounts for errors-in-variables. Using S&P 500 panel data, we analyse the empirical performance of the CAPM and theFama and French (1993)three-factor model. We find that time-variation of betas in the CAPM and the time variation of the coefficients for the size factor (SMB) and the distress factor (HML) in the three-factor model improve the empirical performance. Therefore, our findings are consistent with time variation of firm-specific exposure to market risk, systematic credit risk and systematic size effects. However, a Bayesian model comparison trading off goodness of fit and model complexity indicates that the conditional CAPM performs best, followed by the conditional three-factor model, the unconditional CAPM, and the unconditional three-factor model. [source] MCMC-based linkage analysis for complex traits on general pedigrees: multipoint analysis with a two-locus model and a polygenic componentGENETIC EPIDEMIOLOGY, Issue 2 2007Yun Ju Sung Abstract We describe a new program lm_twoqtl, part of the MORGAN package, for parametric linkage analysis with a quantitative trait locus (QTL) model having one or two QTLs and a polygenic component, which models additional familial correlation from other unlinked QTLs. The program has no restriction on number of markers or complexity of pedigrees, facilitating use of more complex models with general pedigrees. This is the first available program that can handle a model with both two QTLs and a polygenic component. Competing programs use only simpler models: one QTL, one QTL plus a polygenic component, or variance components (VC). Use of simple models when they are incorrect, as for complex traits that are influenced by multiple genes, can bias estimates of QTL location or reduce power to detect linkage. We compute the likelihood with Markov Chain Monte Carlo (MCMC) realization of segregation indicators at the hypothesized QTL locations conditional on marker data, summation over phased multilocus genotypes of founders, and peeling of the polygenic component. Simulated examples, with various sized pedigrees, show that two-QTL analysis correctly identifies the location of both QTLs, even when they are closely linked, whereas other analyses, including the VC approach, fail to identify the location of QTLs with modest contribution. Our examples illustrate the advantage of parametric linkage analysis with two QTLs, which provides higher power for linkage detection and better localization than use of simpler models. Genet. Epidemiol. © 2006 Wiley-Liss, Inc. [source] Bayesian inference strategies for the prediction of genetic merit using threshold models with an application to calving ease scores in Italian Piemontese cattleJOURNAL OF ANIMAL BREEDING AND GENETICS, Issue 4 2002K. Kizilkaya Summary First parity calving difficulty scores from Italian Piemontese cattle were analysed using a threshold mixed effects model. The model included the fixed effects of age of dam and sex of calf and their interaction and the random effects of sire, maternal grandsire, and herd-year-season. Covariances between sire and maternal grandsire effects were modelled using a numerator relationship matrix based on male ancestors. Field data consisted of 23 953 records collected between 1989 and 1998 from 4741 herd-year-seasons. Variance and covariance components were estimated using two alternative approximate marginal maximum likelihood (MML) methods, one based on expectation-maximization (EM) and the other based on Laplacian integration. Inferences were compared to those based on three separate runs or sequences of Markov Chain Monte Carlo (MCMC) sampling in order to assess the validity of approximate MML estimates derived from data with similar size and design structure. Point estimates of direct heritability were 0.24, 0.25 and 0.26 for EM, Laplacian and MCMC (posterior mean), respectively, whereas corresponding maternal heritability estimates were 0.10, 0.11 and 0.12, respectively. The covariance between additive direct and maternal effects was found to be not different from zero based on MCMC-derived confidence sets. The conventional joint modal estimates of sire effects and associated standard errors based on MML estimates of variance and covariance components differed little from the respective posterior means and standard deviations derived from MCMC. Therefore, there may be little need to pursue computation-intensive MCMC methods for inference on genetic parameters and genetic merits using conventional threshold sire and maternal grandsire models for large datasets on calving ease. Zusammenfassung Die Kalbeschwierigkeiten bei italienischen Piemonteser Erstkalbskühen wurden mittels eines gemischten Threshold Modells untersucht. Im Modell wurden die fixen Einflüsse vom Alter der Kuh und dem Geschlecht des Kalbes, der Interaktion zwischen beiden und die zufälligen Effekte des Großvaters der Mutter und der Herden-Jahr-Saisonklasse berücksichtigt. Die Kovarianz zwischen dem Vater der Kuh und dem Großvater der Mutter wurde über die nur auf väterlicher Verwandtschaft basierenden Verwandtschaftsmatrix berücksichtigt. Es wurden insgesamt 23953 Datensätze aus den Jahren 1989 bis 1998 von 4741 Herden-Jahr-Saisonklassen ausgewertet. Die Varianz- und Kovarianzkomponenten wurden mittels zweier verschiedener approximativer marginal Maximum Likelihood (MML) Methoden geschätzt, die erste basierend auf Expectation-Maximierung (EM) und die zweite auf Laplacian Integration. Rückschlüsse wurden verglichen mit solchen, basierend auf drei einzelne Läufe oder Sequenzen von Markov Chain Monte Carlo (MCMC) Stichproben, um die Gültigkeit der approximativen MML Schätzer aus Daten mit ähnlicher Größe und Struktur zu prüfen. Die Punktschätzer der direkten Heritabilität lagen bei 0,24; 0,25 und 0,26 für EM, Laplacian und MCMC (Posterior Mean), während die entsprechenden maternalen Heritabilitäten bei 0,10, 0,11 und 0,12 lagen. Die Kovarianz zwischen dem direkten additiven und dem maternalen Effekt wurden als nicht von Null verschieden geschätzt, basierend auf MCMC abgeleiteten Konfidenzintervallen. Die konventionellen Schätzer der Vatereffekte und deren Standardfehler aus den MML-Schätzungen der Varianz- und Kovarianzkomponenten differieren leicht von denen aus der MCMC Analyse. Daraus folgend besteht wenig Bedarf die rechenintensiven MCMC-Methoden anzuwenden, um genetische Parameter und den genetischen Erfolg zu schätzen, wenn konventionelle Threshold Modelle für große Datensätze mit Vätern und mütterlichen Großvätern mit Kalbeschwierigkeiten genutzt werden. [source] Generalizability in Item Response ModelingJOURNAL OF EDUCATIONAL MEASUREMENT, Issue 2 2007Derek C. Briggs An approach called generalizability in item response modeling (GIRM) is introduced in this article. The GIRM approach essentially incorporates the sampling model of generalizability theory (GT) into the scaling model of item response theory (IRT) by making distributional assumptions about the relevant measurement facets. By specifying a random effects measurement model, and taking advantage of the flexibility of Markov Chain Monte Carlo (MCMC) estimation methods, it becomes possible to estimate GT variance components simultaneously with traditional IRT parameters. It is shown how GT and IRT can be linked together, in the context of a single-facet measurement design with binary items. Using both simulated and empirical data with the software WinBUGS, the GIRM approach is shown to produce results comparable to those from a standard GT analysis, while also producing results from a random effects IRT model. [source] The use of marker-based relationship information to estimate the heritability of body weight in a natural population: a cautionary taleJOURNAL OF EVOLUTIONARY BIOLOGY, Issue 1 2002S. C. Thomas A number of procedures have been developed that allow the genetic parameters of natural populations to be estimated using relationship information inferred from marker data rather than known pedigrees. Three published approaches are available; the regression, pair-wise likelihood and Markov Chain Monte Carlo (MCMC) sib-ship reconstruction methods. These were applied to body weight and molecular data collected from the Soay sheep population of St. Kilda, which has a previously determined pedigree. The regression and pair-wise likelihood approaches do not specify an exact pedigree and yielded unreliable heritability estimates, that were sensitive to alteration of the fixed effects. The MCMC method, which specifies a pedigree prior to heritability estimation, yielded results closer to those determined using the known pedigree. In populations of low average relationship, such as the Soay sheep population, determination of a reliable pedigree is more useful than indirect approaches that do not specify a pedigree. [source] Approaches to Evaluate Water Quality Model Parameter Uncertainty for Adaptive TMDL Implementation,JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, Issue 6 2007Craig A. Stow Abstract:, The National Research Council recommended Adaptive Total Maximum Daily Load implementation with the recognition that the predictive uncertainty of water quality models can be high. Quantifying predictive uncertainty provides important information for model selection and decision-making. We review five methods that have been used with water quality models to evaluate model parameter and predictive uncertainty. These methods (1) Regionalized Sensitivity Analysis, (2) Generalized Likelihood Uncertainty Estimation, (3) Bayesian Monte Carlo, (4) Importance Sampling, and (5) Markov Chain Monte Carlo (MCMC) are based on similar concepts; their development over time was facilitated by the increasing availability of fast, cheap computers. Using a Streeter-Phelps model as an example we show that, applied consistently, these methods give compatible results. Thus, all of these methods can, in principle, provide useful sets of parameter values that can be used to evaluate model predictive uncertainty, though, in practice, some are quickly limited by the "curse of dimensionality" or may have difficulty evaluating irregularly shaped parameter spaces. Adaptive implementation invites model updating, as new data become available reflecting water-body responses to pollutant load reductions, and a Bayesian approach using MCMC is particularly handy for that task. [source] The 1,1000 ,m spectral energy distributions of far-infrared galaxiesMONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY, Issue 2 2006A. Sajina ABSTRACT Galaxies selected at 170 ,m by the Infrared Space Observatory (ISO) Far-IR BACKground (FIRBACK) survey represent the brightest ,10 per cent of the cosmic infrared background. Examining their nature in detail is therefore crucial for constraining models of galaxy evolution. Here, we combine Spitzer archival data with previous near-infrared (near-IR), far-IR, and submillimetre (submm) observations of a representative sample of 22 FIRBACK galaxies spanning three orders of magnitude in IR luminosity. We fit a flexible, multicomponent, empirical SED model of star-forming galaxies designed to model the entire ,1,1000 ,m wavelength range. The fits are performed with a Markov Chain Monte Carlo (MCMC) approach, allowing for meaningful uncertainties to be derived. This approach also highlights degeneracies such as between Td and ,, which we discuss in detail. From these fits and standard relations we derive: LIR, LPAH, star formation rate (SFR), ,V, M*, Mdust, Td, and ,. We look at a variety of correlations between these and combinations thereof in order to examine the physical nature of these galaxies. Our conclusions are supplemented by morphological examination of the sources, and comparison with local samples. We find the bulk of our sample to be consistent with fairly standard size and mass disc galaxies with somewhat enhanced star formation relative to local spirals, but likely not bona fide starbursts. A few higher- z luminous infrared galaxies (LIGs) and ultraluminous infrared galaxies (ULIGs) are also present, but contrary to expectation, they are weak mid-IR emitters and overall are consistent with star formation over an extended cold region rather than concentrated in the nuclear regions. We discuss the implications of this study for understanding populations detected at other wavelengths, such as the bright 850-,m Submillimetre Common-User Bolometer Array (SCUBA) sources or the faint Spitzer 24-,m sources. [source] Correcting for Survey Misreports Using Auxiliary Information with an Application to Estimating TurnoutAMERICAN JOURNAL OF POLITICAL SCIENCE, Issue 3 2010Jonathan N. Katz Misreporting is a problem that plagues researchers who use survey data. In this article, we develop a parametric model that corrects for misclassified binary responses using information on the misreporting patterns obtained from auxiliary data sources. The model is implemented within the Bayesian framework via Markov Chain Monte Carlo (MCMC) methods and can be easily extended to address other problems exhibited by survey data, such as missing response and/or covariate values. While the model is fully general, we illustrate its application in the context of estimating models of turnout using data from the American National Elections Studies. [source] Estimation of Poisson Rates with Misclassified CountsBIOMETRICAL JOURNAL, Issue 8 2002Thomas L. Bratcher Abstract The Poisson assumption is popular when data arises in the form of counts. In many applications such counts are fallible. Little research has been done on the Poisson distribution when both false positives and false negatives are present. We present a model in this paper that corrects for misclassification of count data. Bayesian estimators are developed. We provide the actual posterior distributions via integration. Markov Chain Monte Carlo results, which are more convenient for large sample sizes, are utilized for inference. [source] | |||||||||||||