Statistical Models Commons^™

Bayesian Analysis For Penalized Spline Regression Using Win Bugs, Ciprian M. Crainiceanu, David Ruppert, M.P. Wand

Johns Hopkins University, Dept. of Biostatistics Working Papers

Penalized splines can be viewed as BLUPs in a mixed model framework, which allows the use of mixed model software for smoothing. Thus, software originally developed for Bayesian analysis of mixed models can be used for penalized spline regression. Bayesian inference for nonparametric models enjoys the flexibility of nonparametric models and the exact inference provided by the Bayesian inferential machinery. This paper provides a simple, yet comprehensive, set of programs for the implementation of nonparametric Bayesian analysis in WinBUGS. MCMC mixing is substantially improved from the previous versions by using low{rank thin{plate splines instead of truncated polynomial basis. Simulation time …

Assessing Population Level Genetic Instability Via Moving Average, Samuel Mcdaniel, Rebecca Betensky, Tianxi Cai

Harvard University Biostatistics Working Paper Series

No abstract provided.

Implementation Of Uncertainty Propagation In Triton/Keno: To Support The Global Nuclear Energy Partnership, Charlotta Sanders, Denis Beller

Reactor Campaign (TRP)

Monte Carlo methods are beginning to be used for three-dimensional fuel depletion analyses to compute various quantities of interest, including isotopic compositions of used fuel.1 The TRITON control module, available in the SCALE 5.1 code system, can perform three dimensional (3-D) depletion calculations using either the KENO V.a or KENO-VI Monte Carlo transport codes, as well as the two-dimensional (2- D) NEWT discrete ordinates code. For typical reactor systems, the neutron flux is not spatially uniform. For Monte Carlo simulations, this results in non-uniform statistical uncertainties in the computed reaction rates. For spatial regions where the flux is low, e.g., …

Monaco/Mavric Evaluation For Facility Shielding And Dose Rate Analysis: To Support The Global Nuclear Energy Partnership, Charlotta Sanders, Denis Beller

Reactor Campaign (TRP)

Monte Carlo methods are used to compute fluxes or dose rates over large areas using mesh tallies. For problems that demand that the uncertainty in each mesh cell be less than some set maximum, computation time is controlled by the cell with the largest uncertainty. This issue becomes quite troublesome in deep-penetration problems, and advanced variance reduction techniques are required to obtain reasonable uncertainties over large areas.

In this project the MAVRIC sequence will be evaluated along with the Monte Carlo engine Monaco to investigate its effectiveness and usefulness in facility shielding and dose rate analyses. A previously MCNP-evaluated cask …

Analysis Of Shanghai Port's Competitiveness After The Operation Of Yangshan Deep-Water Port, Wei Jiang

World Maritime University Dissertations

No abstract provided.

Choice And Optimization Of Forecasting Models For Container Port Throughput, Qingcheng Xue

World Maritime University Dissertations

No abstract provided.

Research Of Be550 Rigs Transportation Projects, Lifeng Shi

World Maritime University Dissertations

No abstract provided.

Research On The Status Quo And Operational Modes Of Chinese Petrochemical Logistics, Jiajie Hou

World Maritime University Dissertations

No abstract provided.

Study On Grade Evaluation Of Port Shoreline In Jiangsu Province, Wei Li

World Maritime University Dissertations

No abstract provided.

The Logistics Supplier Selection Of Sgm With Ahp Method, Jian Wang

World Maritime University Dissertations

No abstract provided.

Research On Competitiveness Of Dalian Port, Zhaobo Xu

World Maritime University Dissertations

No abstract provided.

The Black Swan: Praise And Criticism, Peter H. Westfall, Joseph M. Hilbe

Joseph M Hilbe

No abstract provided.

Super Learner, Mark J. Van Der Laan, Eric C. Polley, Alan E. Hubbard

U.C. Berkeley Division of Biostatistics Working Paper Series

Previous articles (van der Laan and Dudoit (2003); van der Laan et al. (2006); Sinisi et al. (2007)) advertised and theoretically validated the use of cross-validation to select among many candidate estimators to compute a so called super learner which outperforms any of the given candidate estimators. The theoretical basis was provided for this super learner based on oracle results for the cross-validation selector (e.g., van der Laan and Dudoit (2003); van der Laan et al. (2006)) and in Sinisi et al. (2007). In addition, these papers contained a practical demonstration of the adaptivity of this so called super learner …

Random Effects Models In A Meta-Analysis Of The Accuracy Of Diagnostic Tests Within A Gold Standard In The Presence Of Missing Data, Haitao Chu, Sining Chen, Thomas A. Louis

Johns Hopkins University, Dept. of Biostatistics Working Papers

In evaluating the accuracy of diagnosis tests, it is common to apply two imperfect tests jointly or sequentially to a study population. In a recent meta-analysis of the accuracy of microsatellite instability testing (MSI) and traditional mutation analysis (MUT) in predicting germline mutations of the mismatch repair (MMR) genes, a Bayesian approach (Chen, Watson, and Parmigiani 2005) was proposed to handle missing data resulting from partial testing and the lack of a gold standard. In this paper, we demonstrate an improved estimation of the sensitivities and specificities of MSI and MUT by using a nonlinear mixed model and a Bayesian …

Bayesian Bivariate Image Analysis With Application To Dual Autoradiography, Timothy D. Johnson, Morand Piert

The University of Michigan Department of Biostatistics Working Paper Series

We present a Bayesian bivariate image model and apply it to a study that was designed to investigate the relationship between hypoxia and angiogenesis in an animal tumor model. Two radiolabeled tracers (one measuring angio- genesis, the other measuring hypoxia) were simultaneously injected into the animals, the tumors removed and autoradiographic images of the tracer concentrations were obtained. We model correlation between tracers with a mixture of bivariate normal distributions and the spatial correlation inherent in the images by means of the celebrated Potts model. Although the Potts model is typically used for image segmentation, we use it solely as …

Quantitative Magnetic Resonance Image Analysis Via The Em Algorithm With Stochastic Variation, Xiaoxi Zhang, Timothy D. Johnson, Roderick J.A. Little

The University of Michigan Department of Biostatistics Working Paper Series

Quantitative Magnetic Resonance Imaging (qMRI) provides researchers insight into pathological and physiological alterations of living tissue, with the help of which, researchers hope to predict (local) therapeutic efficacy early and determine optimal treatment schedule. However, the analysis of qMRI has been limited to ad-hoc heuristic methods. Our research provides a powerful statistical framework for image analysis and sheds light on future localized adaptive treatment regimes tailored to the individual’s response. We assume in an imperfect world we only observe a blurred and noisy version of the underlying “true” scene via qMRI, due to measurement errors or unpredictable influences. We use …

Bayesian Spatial Modeling Of Fmri Data: A Multiple-Subject Analysis, Lei Xu, Timothy Johnson, Thomas Nichols

The University of Michigan Department of Biostatistics Working Paper Series

The aim of this work is to develop a spatial model for multi-subject fMRI data. While there has been much work on univariate modeling of each voxel for single- and multi-subject data, and some work on spatial modeling for single-subject data, there has been no work on spatial models that explicitly account for intersubject variability in activation location. We use a Bayesian hierarchical spatial model to fit the data. At the first level we model "population centers" that mark the centers of regions of activation. For a given population center each subject may have zero or more associated "individual components". …

The Time Invariance Principle, Ecological (Non)Chaos, And A Fundamental Pitfall Of Discrete Modeling, Bo Deng

Department of Mathematics: Faculty Publications

This paper is to show that most discrete models used for population dynamics in ecology are inherently pathological that their predications cannot be independently verified by experiments because they violate a fundamental principle of physics. The result is used to tackle an on-going controversy regarding ecological chaos. Another implication of the result is that all continuous dynamical systems must be modeled by differential equations. As a result it suggests that researches based on discrete modeling must be closely scrutinized and the teaching of calculus and differential equations must be emphasized for students of biology.

A Survey Of The Likelihood Approach To Bioequivalence Trials, Leena Choi, Brian S. Caffo, Charles Rohde

Johns Hopkins University, Dept. of Biostatistics Working Papers

Bioequivalence trials are abbreviated clinical trials whereby a generic drug or new formulation is evaluated to determine if it is "equivalent" to a corresponding previously approved brand-name drug or formulation. In this manuscript, we survey the process of testing bioequivalence and advocate the likelihood paradigm for representing the resulting data as evidence. We emphasize the unique conflicts between hypothesis testing and confidence intervals in this area - which we believe are indicative of the existence of the systemic defects in the frequentist approach - that the likelihood paradigm avoids. We suggest the direct use of profile likelihoods for evaluating bioequivalence …

Mortality In The Medicare Population And Chronic Exposure To Fine Particulate Air Pollution , Scott L. Zeger, Francesca Dominici, Aidan Mcdermott, Jonathan M. Samet

Johns Hopkins University, Dept. of Biostatistics Working Papers

Prospective cohort studies have provided evidence on longer-term mortality risks of fine particulate matter (PM2.5), but due to their complexity and costs, only a few have been conducted.

By linking monitoring data to the U.S. Medicare system by county of residence, we developed a retrospective cohort study, the Medicare Air Pollution Cohort Study (MCAPS), comprising over 20 million enrollees in the 250 largest counties during 2000-2002. We estimated log-linear regression models having as outcome the age-specific mortality rate for each county and as the main predictor, the average level for the study period 2000. Area-level covariates were used to adjust …

A Bayesian Hierarchical Model For Constrained Distributed Lag Functions: Estimating The Time Course Of Hospitalization Associated With Air Pollution Exposure, Roger Peng, Francesca Dominici, Leah J. Welty

Johns Hopkins University, Dept. of Biostatistics Working Papers

Numerous time series studies have provided strong evidence of an association between increased levels of ambient air pollution and increased levels of hospital admissions, typically at 0, 1, or 2 days after an air pollution episode. An important research aim is to extend existing statistical models so that a more detailed understanding of the time course of hospitalization after exposure to air pollution can be obtained. Information about this time course, combined with prior knowledge about biological mechanisms, could provide the basis for hypotheses concerning the mechanism by which air pollution causes disease. Previous studies have identified two important methodological …

Diffusion And Fractional Diffusion Based Models For Multiple Light Scattering And Image Analysis, Jonathan Blackledge

Articles

This paper considers a fractional light diffusion model as an approach to characterizing the case when intermediate scattering processes are present, i.e. the scattering regime is neither strong nor weak. In order to introduce the basis for this approach, we revisit the elements of formal scattering theory and the classical diffusion problem in terms of solutions to the inhomogeneous wave and diffusion equations respectively. We then address the significance of these equations in terms of a random walk model for multiple scattering. This leads to the proposition of a fractional diffusion equation for modelling intermediate strength scattering that is based …

The Extent Of Interaction Between The Scallop And Prawn Fleets In The Shark Bay Scallop Managed Fishery, John Dickson

Theses : Honours

The Shark Bay Managed Scallop Fishery is Western Australia's most important scallop fishery with an annual value of between $2 and $58 million. In addition to this the fishery is an important source of regional employment with approximately 160 skippers and crew employed during the 2005 season. Two separate fleets are permitted to fish for scallops in this fishery, the first consisting of dedicated scallop fishing vessels (Class A licences) and the second of prawn fishing vessels (Class B licences) that are allowed to take scallops under a catch sharing arrangement. Concerns exist over the interactions between these two fleets …

Modeling And Efficient Estimation Of Intra-Family Correlations, Roy Sabo

Mathematics & Statistics Theses & Dissertations

Familial data occur when observations are taken on multiple members of the same family. Due to relationships between these members, both genetic and by cohabitation, their response variables will likely exhibit some form of dependence. Most of the existing literature models this dependence with an equicorrelated structure. This structure is appropriate when the dependencies between family members are similar, such as in genetic studies, but not in cases where we expect the dependencies to differ, such as behavioral comparisons across different age groups. In this dissertation we first discuss an alternative structure based upon first-order autoregressive correlation. Specifically we create …