Consider the exchangeable Bayesian hierarchical model where observations y(i) are independently distributed from sampling densities with unknown means, the means mu(i) are a random sample from a distribution g, and th...
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Consider the exchangeable Bayesian hierarchical model where observations y(i) are independently distributed from sampling densities with unknown means, the means mu(i) are a random sample from a distribution g, and the parameters of g are assigned a known distribution h. A simple algorithm is presented for summarizing the posterior distribution based on Gibbs sampling and the metropolis algorithm. The software program Matlab is used to implement the algorithm and provide a graphical output analysis. An binomial example is used to illustrate the flexibility of modeling possible using this algorithm. Methods of model checking and extensions to hierarchical regression modeling are discussed.
We develop a hierarchical Bayesian approach for inference in random coefficient dynamic panel data models. Our approach allows for the initial values of each unit's process to be correlated with the unit-specific ...
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We develop a hierarchical Bayesian approach for inference in random coefficient dynamic panel data models. Our approach allows for the initial values of each unit's process to be correlated with the unit-specific coefficients. We impose a stationarity assumption for each unit's process by assuming that the unit-specific autoregressive coefficient is drawn from a logitnormal distribution. Our method is shown to have favorable properties compared to the mean group estimator in a Monte Carlo study. We apply our approach to analyze energy and protein intakes among individuals from the Philippines.
This paper describes a new scheduling solution for large number multi-product batch processes with complex intermediate storage system. Recently many batch chemical industries have turned their attention to a more eff...
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This paper describes a new scheduling solution for large number multi-product batch processes with complex intermediate storage system. Recently many batch chemical industries have turned their attention to a more efficient system known as a pipeless batch system. But existing plants need to change their systems to pipeless systems, piece by piece. In this case, current systems are changed to pipeless systems by way of non critical process operations such as through the use of intermediate storage. We have taken the conventional batch plant with a pipeless storage system as an objective process. Although the operation of a pipeless storage system becomes more complex, its efficiency is very high. With this system, all of the storage should be commonly used by any batch unit. For this reason, solving the optimal scheduling problem of this system with a mathematical method is very difficult. Despite the attempts of many previous researches, there has been no contribution which solves the scheduling of intermediate storage for complex batch processes. In this paper, we have developed a hybrid system of heuristics and Simulated Annealing (SA) for large multi-product processes using a pipeless storage system. The results of this study show that the performance and computational time of this method are superior to that of SA and Rapid Access Extensive Search (RAES) methods.
This article provides a first theoretical analysis of a new Monte Carlo approach, the dynamic weighting algorithm, proposed recently by Wong and Liang. In dynamic weighting Monte Carlo, one augments the original stale...
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This article provides a first theoretical analysis of a new Monte Carlo approach, the dynamic weighting algorithm, proposed recently by Wong and Liang. In dynamic weighting Monte Carlo, one augments the original stale space of interest by a weighting factor, which allows the resulting Markov chain to move more freely and to escape from local modes. II uses a new invariance principle to guide the construction of transition rules. We analyze the behavior of the weights resulting from such a process and provide detailed recommendations on how to use these weights properly. Our recommendations;are supported by a renewal theory-type analysis. Our theoretical investigations are further demonstrated by a simulation study and applications in neural network training and Ising model simulations.
Incumbency advantage is one of the most widely studied features in American legislative elections. In this article we construct and implement an estimate that allows incumbency advantage to vary between individual inc...
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Incumbency advantage is one of the most widely studied features in American legislative elections. In this article we construct and implement an estimate that allows incumbency advantage to vary between individual incumbents. This model predicts that open-seat elections will be less variable than those with incumbents running, an observed empirical pattern that is not explained by previous models. We apply our method to the U.S. House of Representatives in the twentieth century. Our estimate of the overall pattern of incumbency advantage over time is similar to previous estimates (although slightly lower), and we also find a pattern of increasing variation. More generally, our multilevel model represents a new method for estimating effects in before-after studies.
The geometrical convergence of the Gibbs sampler for simulating a probability distribution in R-d is proved. The distribution has a density which is a bounded perturbation of a log-concave function and satisfies some ...
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The geometrical convergence of the Gibbs sampler for simulating a probability distribution in R-d is proved. The distribution has a density which is a bounded perturbation of a log-concave function and satisfies some growth conditions. The analysis is based on a representation of the Gibbs sampler and some powerful results from the theory of Harris recurrent Markov chains. (C) 1998 Academic Press.
Early detection of pancreatic cancer is promising for improving clinical outcome;however, no effective biomarker has yet been identified. Here, we detected 61 clinical serum parameters in 200 healthy controls (Ctrls),...
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Early detection of pancreatic cancer is promising for improving clinical outcome;however, no effective biomarker has yet been identified. Here, we detected 61 clinical serum parameters in 200 healthy controls (Ctrls), 163 pancreatic ductal adenocarcinoma (PDAC) patients and 109 benign pancreatitis patients (Benign) in the training group. A metropolis algorithm with Monte Carlo simulation was used for identifying parameter panels. Sera from 183 Ctrl, 129 PDAC and 95 Benign individuals were used for cross-validation. Samples from 77 breast, 72 cervical, 101 colorectal, 138 gastric, 108 prostate and 132 lung cancer patients were collected for evaluating cancer selectivity. A panel consisting of carbohydrate antigen (CA)19-9, albumin (ALB), C-reactive protein (CRP) and interleukin (IL)-8 had the highest diagnostic value for discriminating between PDAC and Ctrl. The sensitivity (SN) was 99.39% for all-stage, 96.10% for early-stage and 98.80% for advanced-stage PDAC at 90% specificity (SP). In the validation group, the sensitivities were 93.80, 93.10 and 94.40%, respectively, at 90% SP. This panel also identified 80.52% of the breast cancer, 66.67% cervical cancer, 86.14% colorectal cancer, 89.86% gastric cancer, 71.30% prostate cancer and 93.85% lung cancer samples as non-PDAC. The panel consisting of CA19-9, carbon dioxide, CRP and IL-6 panel had the highest diagnostic value for discriminating between PDAC and Benign. The SN was 74.23% for all-stage, 75.30% for early-stage and 74.40% for advanced-stage PDAC at 90% SP. In the validation group, the sensitivities were 72.10, 76.10 and 67.20%, respectively, at 90% SP. Our parameter panels may aid in the early detection of PDAC to improve clinical outcome. What's new? Few people survive a pancreatic cancer diagnosis, in part because it's rarely detected early. In this study, the authors went looking for biomarkers that could indicate a developing cancer. They tested 61 different biomarkers in blood samples from patients with
We look at adaptive Markov chain Monte Carlo algorithms that generate stochastic processes based on sequences of transition kernels, where each transition kernel is allowed to depend on the history of the process. We ...
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We look at adaptive Markov chain Monte Carlo algorithms that generate stochastic processes based on sequences of transition kernels, where each transition kernel is allowed to depend on the history of the process. We show under certain conditions that the stochastic process generated is ergodic, with appropriate stationary distribution. We use this result to analyse an adaptive version of the random walk metropolis algorithm where the scale parameter sigma is sequentially adapted using a Robbins-Monro type algorithm in order to find the optimal scale parameter sigma(opt). We close with a simulation example.
Likelihood computation in spatial statistics requires accurate and efficient calculation of the normalizing constant (i.e. partition function) of the Gibbs distribution of the model. Two available methods to calculate...
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Likelihood computation in spatial statistics requires accurate and efficient calculation of the normalizing constant (i.e. partition function) of the Gibbs distribution of the model. Two available methods to calculate the normalizing constant by Markov chain Monte Carlo methods are compared by simulation experiments for an Ising model, a Gaussian Markov field model and a pairwise interaction point field model.
A human dose response model for Escherichia coli O157 would enable prediction of risk of infection to humans following exposure from either foodborne or environmental pathways. However, due to the severe nature of the...
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A human dose response model for Escherichia coli O157 would enable prediction of risk of infection to humans following exposure from either foodborne or environmental pathways. However, due to the severe nature of the disease, volunteer human dose response studies cannot be carried out. Surrogate models from Shigella fed to humans and E. coli O157 to rabbits have been utilised but are significantly different to one another. In addition data obtained by animal exposure may not be representative for human beings. An alternative approach to generating and validating a dose response model is to use quantitative data obtained from actual human outbreaks. This work collates outbreak data obtained from global sources and these are fitted using exponential and beta-Poisson models. The best fitting model was found to be the beta-Poisson model using a beta-binomial likelihood and the authors favour the exact version of this model. The confidence levels in this model encompass a previously published Shigella dose response model. The potential incorporation of this model into QMRAs is discussed together with applications of the model to help explain foodborne outbreaks. (c) 2005 Elsevier B.V. All rights reserved.
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