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Bayesian Estimator (bayesian + estimator)
Selected AbstractsBayesian semiparametric estimation of discrete duration models: an application of the dirichlet process priorJOURNAL OF APPLIED ECONOMETRICS, Issue 1 2001Michele Campolieti This paper proposes a Bayesian estimator for a discrete time duration model which incorporates a non-parametric specification of the unobserved heterogeneity distribution, through the use of a Dirichlet process prior. This estimator offers distinct advantages over the Nonparametric Maximum Likelihood estimator of this model. First, it allows for exact finite sample inference. Second, it is easily estimated and mixed with flexible specifications of the baseline hazard. An application of the model to employment duration data from the Canadian province of New Brunswick is provided. Copyright © 2001 John Wiley & Sons, Ltd. [source] Pedigree reconstruction in wild cichlid fish populationsMOLECULAR ECOLOGY, Issue 20 2008MARTIN KOCH Abstract It is common practice to use microsatellites to detect parents and their offspring in wild and captive populations, in order to reconstruct a pedigree. However, correct inference is often constrained by a number of factors, including the absence of demographic data and ignorance regarding the completeness of parental sampling. Here we present a new Bayesian estimator that simultaneously estimates the pedigree and the size of the unsampled population. The method is robust to genotyping error, and can estimate pedigrees in the absence of demographic data. Using a large-scale microsatellite assay in four wild cichlid fish populations of Lake Tanganyika (1000 individuals in total), we assess the performance of the Bayesian estimator against the most popular assignment program, Cervus. We found small but significant pedigrees in each of the tested populations using the Bayesian procedure, but Cervus had very high type I error rates when the size of the unsampled population was assumed to be lower than what it was. The need of pedigree relationships to infer adaptive processes in natural populations places strong constraints on sampling design and identification of multigenerational pedigrees in natural populations. [source] A bayesian estimator for the dependence function of a bivariate extreme-value distributionTHE CANADIAN JOURNAL OF STATISTICS, Issue 3 2008Simon Guillotte Abstract Any continuous bivariate distribution can be expressed in terms of its margins and a unique copula. In the case of extreme-value distributions, the copula is characterized by a dependence function while each margin depends on three parameters. The authors propose a Bayesian approach for the simultaneous estimation of the dependence function and the parameters defining the margins. They describe a nonparametric model for the dependence function and a reversible jump Markov chain Monte Carlo algorithm for the computation of the Bayesian estimator. They show through simulations that their estimator has a smaller mean integrated squared error than classical nonparametric estimators, especially in small samples. They illustrate their approach on a hydrological data set. Un estimateur bayésien de la fonction de dépendance d'une loi des valeurs extrêmes bivariée Toute loi bivariée continue peut s'écrire en fonction de ses marges et d'une copule unique. Dans le cas des lois des valeurs extrêmes, la copule est caractérisée par une fonction de dépendance tandis que chaque marge dépend de trois paramètres. Les auteurs proposent une approche bayésienne pour l'estimation simultanée de la fonction de dépendance et des paramètres définissant les marges. Ils décrivent un modèle non paramétrique pour la fonction de dépendance et un algorithme MCMC à sauts réversibles pour le calcul de l'estimateur bayésien. Ils montrent par simulation que l'erreur quadratique moyenne intégrée de leur estimateur est plus faible que celle des estimateurs classiques, surtout dans de petits échantillons. Ils illustrent leur propos à l'aide de données hydrologiques. [source] Delivery horizon and grain market volatilityTHE JOURNAL OF FUTURES MARKETS, Issue 9 2010Berna Karali We study the difference in the volatility dynamics of CBOT corn, soybeans, and oats futures prices across different delivery horizons via a smoothed Bayesian estimator. We find that futures price volatilities in these markets are affected by inventories, time to delivery, and the crop progress period and that there are important differences in the effects across delivery horizons. We also find that price volatility is higher before the harvest starts in most cases compared to the volatility during the planting period. These results have implications for hedging, options pricing, and the setting of margin requirements. © 2010 Wiley Periodicals, Inc. Jrl Fut Mark 30:846,873, 2010 [source] Mycophenolic acid area under the curve correlates with disease activity in lupus patients treated with mycophenolate mofetilARTHRITIS & RHEUMATISM, Issue 7 2010Noël Zahr Objective Mycophenolic acid (MPA) is the active metabolite of mycophenolate mofetil (MMF), which is widely used to treat systemic lupus erythematosus (SLE). In transplantation, MPA area under the plasma concentration,time curve from 0 to 12 hours (MPA AUC0,12) is correlated with clinical outcome. We undertook the present study to assess possible relationships between SLE activity and MPA AUC0,12. Methods Using a Bayesian estimator, MPA AUC0,12 was determined in 71 consecutive SLE patients (61 women and 10 men; mean ± SD age 34 ± 10 years) receiving a stable MMF dose. On the same day, SLE activity was assessed using the SLE Disease Activity Index (SLEDAI; active disease defined as a SLEDAI score ,6) and the British Isles Lupus Assessment Group (BILAG) index (active disease defined as BILAG A or B). Results Two groups were studied: patients with active SLE (mean ± SD SLEDAI score 11.6 ± 4.4; n = 26) and patients with inactive SLE (mean ± SD SLEDAI score 1.9 ± 1.6; n = 45). MPA AUC0,12 correlated weakly with the dose of MMF (r = 0.33, P = 0.005). Mean ± SD MPA AUC0,12 in the group with active SLE was significantly lower than that in the group with inactive SLE (26.8 ± 13.6 ,g.hour/ml versus 46.5 ± 16.3 ,g.hour/ml; P < 0.0001). MPA AUC0,12 was negatively correlated with the SLEDAI (r = ,0.64, P < 0.0001). In multivariate analysis, MPA AUC0,12 was the sole parameter associated with SLE activity (odds ratio 0.89 [95% confidence interval 0.83,0.96], P = 0.002). The MPA AUC0,12 threshold value of 35 ,g.hour/ml was associated with the lowest risk of active SLE. Conclusion Our data show that SLE activity is strongly correlated with MPA AUC0,12. An individualized dosing regimen of MMF, with a target AUC0,12 of 35 ,g.hour/ml, should be considered for SLE patients. [source] Decision-making in structure solution using Bayesian estimates of map quality: the PHENIX AutoSol wizardACTA CRYSTALLOGRAPHICA SECTION D, Issue 6 2009Thomas C. Terwilliger Estimates of the quality of experimental maps are important in many stages of structure determination of macromolecules. Map quality is defined here as the correlation between a map and the corresponding map obtained using phases from the final refined model. Here, ten different measures of experimental map quality were examined using a set of 1359 maps calculated by re-analysis of 246 solved MAD, SAD and MIR data sets. A simple Bayesian approach to estimation of map quality from one or more measures is presented. It was found that a Bayesian estimator based on the skewness of the density values in an electron-density map is the most accurate of the ten individual Bayesian estimators of map quality examined, with a correlation between estimated and actual map quality of 0.90. A combination of the skewness of electron density with the local correlation of r.m.s. density gives a further improvement in estimating map quality, with an overall correlation coefficient of 0.92. The PHENIX AutoSol wizard carries out automated structure solution based on any combination of SAD, MAD, SIR or MIR data sets. The wizard is based on tools from the PHENIX package and uses the Bayesian estimates of map quality described here to choose the highest quality solutions after experimental phasing. [source] Psychometric Properties of IRT Proficiency EstimatesEDUCATIONAL MEASUREMENT: ISSUES AND PRACTICE, Issue 3 2010Michael J. Kolen Psychometric properties of item response theory proficiency estimates are considered in this paper. Proficiency estimators based on summed scores and pattern scores include non-Bayes maximum likelihood and test characteristic curve estimators and Bayesian estimators. The psychometric properties investigated include reliability, conditional standard errors of measurement, and score distributions. Four real-data examples include (a) effects of choice of estimator on score distributions and percent proficient, (b) effects of the prior distribution on score distributions and percent proficient, (c) effects of test length on score distributions and percent proficient, and (d) effects of proficiency estimator on growth-related statistics for a vertical scale. The examples illustrate that the choice of estimator influences score distributions and the assignment of examinee to proficiency levels. In particular, for the examples studied, the choice of Bayes versus non-Bayes estimators had a more serious practical effect than the choice of summed versus pattern scoring. [source] Application of pharmacokinetic modelling to the routine therapeutic drug monitoring of anticancer drugsFUNDAMENTAL & CLINICAL PHARMACOLOGY, Issue 4 2002Annick Rousseau Abstract Over the last 10 years, proofs of the clinical interest of therapeutic drug monitoring (TDM) of certain anticancer drugs have been established. Numerous studies have shown that TDM is an efficient tool for controlling the toxicity of therapeutic drugs, and a few trials have even demonstrated that it can improve their efficacy. This article critically reviews TDM tools based on pharmacokinetic modelling of anticancer drugs. The administered dose of anticancer drugs is sometimes adjusted individually using either a priori or a posteriori methods. The most frequent clinical application of a priori formulae concerns carboplatin and allows the computation of the first dose based on biometrical and biological data such as weight, age, gender, creatinine clearance and glomerular filtration rate. A posteriori methods use drug plasma concentrations to adjust the subsequent dose(s). Thus, nomograms allowing dose adjustment on the basis of blood concentration are routinely used for 5-fluorouracil given as long continuous infusions. Multilinear regression models have been developed, for example for etoposide, doxorubicin, carboplatin, cyclophosphamide and irinotecan, to predict a single exposure variable [such as area under concentration,time curve (AUC)] from a small number of plasma concentrations obtained at predetermined times after a standard dose. These models can only be applied by using the same dose and schedule as the original study. Bayesian estimation offers more flexibility in blood sampling times and, owing to its precision and to the amount of information provided, is the method of choice for ensuring that a given patient benefits from the desired systemic exposure. Unlike the other a posteriori methods, Bayesian estimation is based on population pharmacokinetic studies and can take into account the effects of different individual factors on the pharmacokinetics of the drug. Bayesian estimators have been used to determine maximum tolerated systemic exposure thresholds (e.g. for topotecan or teniposide) as well as for the routine monitoring of drugs characterized by a very high interindividual pharmacokinetic variability such as methotrexate or carboplatin. The development of these methods has contributed to improving cancer chemotherapy in terms of patient outcome and survival and should be pursued. [source] Bayesian inference for Rayleigh distribution under progressive censored sampleAPPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 3 2006Shuo-Jye Wu Abstract It is often the case that some information is available on the parameter of failure time distributions from previous experiments or analyses of failure time data. The Bayesian approach provides the methodology for incorporation of previous information with the current data. In this paper, given a progressively type II censored sample from a Rayleigh distribution, Bayesian estimators and credible intervals are obtained for the parameter and reliability function. We also derive the Bayes predictive estimator and highest posterior density prediction interval for future observations. Two numerical examples are presented for illustration and some simulation study and comparisons are performed. Copyright © 2006 John Wiley & Sons, Ltd. [source] Decision-making in structure solution using Bayesian estimates of map quality: the PHENIX AutoSol wizardACTA CRYSTALLOGRAPHICA SECTION D, Issue 6 2009Thomas C. Terwilliger Estimates of the quality of experimental maps are important in many stages of structure determination of macromolecules. Map quality is defined here as the correlation between a map and the corresponding map obtained using phases from the final refined model. Here, ten different measures of experimental map quality were examined using a set of 1359 maps calculated by re-analysis of 246 solved MAD, SAD and MIR data sets. A simple Bayesian approach to estimation of map quality from one or more measures is presented. It was found that a Bayesian estimator based on the skewness of the density values in an electron-density map is the most accurate of the ten individual Bayesian estimators of map quality examined, with a correlation between estimated and actual map quality of 0.90. A combination of the skewness of electron density with the local correlation of r.m.s. density gives a further improvement in estimating map quality, with an overall correlation coefficient of 0.92. The PHENIX AutoSol wizard carries out automated structure solution based on any combination of SAD, MAD, SIR or MIR data sets. The wizard is based on tools from the PHENIX package and uses the Bayesian estimates of map quality described here to choose the highest quality solutions after experimental phasing. [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] A bayesian estimator for the dependence function of a bivariate extreme-value distributionTHE CANADIAN JOURNAL OF STATISTICS, Issue 3 2008Simon Guillotte Abstract Any continuous bivariate distribution can be expressed in terms of its margins and a unique copula. In the case of extreme-value distributions, the copula is characterized by a dependence function while each margin depends on three parameters. The authors propose a Bayesian approach for the simultaneous estimation of the dependence function and the parameters defining the margins. They describe a nonparametric model for the dependence function and a reversible jump Markov chain Monte Carlo algorithm for the computation of the Bayesian estimator. They show through simulations that their estimator has a smaller mean integrated squared error than classical nonparametric estimators, especially in small samples. They illustrate their approach on a hydrological data set. Un estimateur bayésien de la fonction de dépendance d'une loi des valeurs extrêmes bivariée Toute loi bivariée continue peut s'écrire en fonction de ses marges et d'une copule unique. Dans le cas des lois des valeurs extrêmes, la copule est caractérisée par une fonction de dépendance tandis que chaque marge dépend de trois paramètres. Les auteurs proposent une approche bayésienne pour l'estimation simultanée de la fonction de dépendance et des paramètres définissant les marges. Ils décrivent un modèle non paramétrique pour la fonction de dépendance et un algorithme MCMC à sauts réversibles pour le calcul de l'estimateur bayésien. Ils montrent par simulation que l'erreur quadratique moyenne intégrée de leur estimateur est plus faible que celle des estimateurs classiques, surtout dans de petits échantillons. Ils illustrent leur propos à l'aide de données hydrologiques. [source] | ||||||||||||||||