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Physical Sciences and Mathematics Commons™
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Articles 31 - 40 of 40
Full-Text Articles in Physical Sciences and Mathematics
Computer Intensive Methods Lecture 3 (Lab 1), Shuangge Ma
Computer Intensive Methods Lecture 3 (Lab 1), Shuangge Ma
Shuangge Ma
No abstract provided.
Computer Intensive Methods Lecture 2, Shuangge Ma
Trans-Dimensional Metropolis-Hastings Using Parallel Chains, Sally Wood, James Pullen, Robert Kohn, David Leslie
Trans-Dimensional Metropolis-Hastings Using Parallel Chains, Sally Wood, James Pullen, Robert Kohn, David Leslie
Sally Wood
A general Bayesian sampling method is developed that uses parallel chains to select between models and to average the predictive density over such models. The method applies to both non-nested models and to nested models, and is particularly useful for mixtures of complex component models, where a novel approach to overcome the label-switching problem is used. The method is illustrated with real and simulated data in model-averaging over alternative financial time series models, mixtures of normal distributions, and mixtures of smoothing spline models.
Priors For A Bayesian Analysis Of Extreme Values, Sally Wood, Julian Wang
Priors For A Bayesian Analysis Of Extreme Values, Sally Wood, Julian Wang
Sally Wood
This article proposes a new prior specification for a Bayesian analysis of the k largest order statistics model. We show that using Jeffreys priors for the end-point and shape parameters of the k largest order statistics model leads to biased estimates of the shape parameter for small to medium sample sizes and to the posterior mode of the end-point being equal to the most extreme observed value. We propose a conjugate prior for the shape parameter and a prior for the end-point which removes the posterior mode at the most extreme observed value while remaining uninformative for values of the …
Computer Intensive Methods Lecture 1, Shuangge Ma
Detection Of Gene Pathways With Predictive Power For Breast Cancer Prognosis, Shuangge Ma
Detection Of Gene Pathways With Predictive Power For Breast Cancer Prognosis, Shuangge Ma
Shuangge Ma
Prognosis of breast cancer is of great scientific and practical interest. Biomedical studies suggest that clinical and environmental risk factors do not have satisfactory predictive power for prognosis. Multiple gene profiling studies have been conducted, searching for predictive genomic measurements. Genes have the inherent pathway structure, where pathways are composed of multiple genes with similar biological functions. The goal of this study is to identify gene pathways with predictive power for breast cancer prognosis. Although multiple pathway analysis methods are available, they have certain drawbacks and are not suitable for the proposed analysis. In this article, we develop a new …
Inferring Information Frequency And Quality, Douglas G. Steigerwald, John Owens
Inferring Information Frequency And Quality, Douglas G. Steigerwald, John Owens
Douglas G. Steigerwald
We develop a microstructure model that, in contrast to previous models, allows one to estimate the frequency and quality of private information. In addition, the model produces stationary asset price and trading volume series. We find evidence that information arrives frequently within a day and that this information is of high quality. The frequent arrival of information, while in contrast to previous microstructure model estimates, accords with nonmodel-based estimates and the related literature testing the mixture-of-distributions hypothesis. To determine if the estimates are correctly reflecting the arrival of latent information, we estimate the parameters over half-hour intervals within the day. …
A Bayesian Approach To Bivariate Nonparametric Regression, Michael Smith, Robert Kohn
A Bayesian Approach To Bivariate Nonparametric Regression, Michael Smith, Robert Kohn
Michael Stanley Smith
No abstract provided.
Nonparametric Regression Using Bayesian Variable Selection, Michael Smith, Robert Kohn
Nonparametric Regression Using Bayesian Variable Selection, Michael Smith, Robert Kohn
Michael Stanley Smith
No abstract provided.
Finite Sample Performance Of Robust Bayesian Regression, Michael Smith, Sheather Simon, Kohn Robert
Finite Sample Performance Of Robust Bayesian Regression, Michael Smith, Sheather Simon, Kohn Robert
Michael Stanley Smith
No abstract provided.