共查询到20条相似文献,搜索用时 0 毫秒
1.
《Journal of Statistical Computation and Simulation》2012,82(4):820-840
Bayesian hierarchical spatio-temporal models are becoming increasingly important due to the increasing availability of space-time data in various domains. In this paper we develop a user friendly R package, spTDyn, for spatio-temporal modelling. It can be used to fit models with spatially varying and temporally dynamic coefficients. The former is used for modelling the spatially varying impact of explanatory variables on the response caused by spatial misalignment. This issue can arise when the covariates only vary over time, or when they are measured over a grid and hence do not match the locations of the response point-level data. The latter is to examine the temporally varying impact of explanatory variables in space-time data due, for example, to seasonality or other time-varying effects. The spTDyn package uses Markov chain Monte Carlo sampling written in C, which makes computations highly efficient, and the interface is written in R making these sophisticated modelling techniques easily accessible to statistical analysts. The models and software, and their advantages, are illustrated using temperature and ozone space-time data. 相似文献
2.
Brian J. Smith Mary Kathryn Cowles 《Journal of the Royal Statistical Society. Series C, Applied statistics》2007,56(3):313-326
Summary. Because exposure to radon gas in buildings is a likely risk factor for lung cancer, estimation of residential radon levels is an important public health endeavour. Radon originates from uranium, and therefore data on the geographical distribution of uranium in the Earth's surface may inform about radon levels. We fit a Bayesian geostatistical model that appropriately combines data on uranium with measurements of indoor home radon in the state of Iowa, thereby obtaining more accurate and precise estimation of the geographic distribution of average residential radon levels than would be possible by using radon data alone. 相似文献
3.
Gang Han Yangxin Huang Qizhai Li Lili Chen Xi Zhang 《Journal of applied statistics》2013,40(11):2516-2532
Modelling of HIV dynamics in AIDS research has greatly improved our understanding of the pathogenesis of HIV-1 infection and guided for the treatment of AIDS patients and evaluation of antiretroviral therapies. Some of the model parameters may have practical meanings with prior knowledge available, but others might not have prior knowledge. Incorporating priors can improve the statistical inference. Although there have been extensive Bayesian and frequentist estimation methods for the viral dynamic models, little work has been done on making simultaneous inference about the Bayesian and frequentist parameters. In this article, we propose a hybrid Bayesian inference approach for viral dynamic nonlinear mixed-effects models using the Bayesian frequentist hybrid theory developed in Yuan [Bayesian frequentist hybrid inference, Ann. Statist. 37 (2009), pp. 2458–2501]. Compared with frequentist inference in a real example and two simulation examples, the hybrid Bayesian approach is able to improve the inference accuracy without compromising the computational load. 相似文献
4.
Jun Yan Mary Kathryn Cowles Shaowen Wang Marc P. Armstrong 《Statistics and Computing》2007,17(4):323-335
When MCMC methods for Bayesian spatiotemporal modeling are applied to large geostatistical problems, challenges arise as a
consequence of memory requirements, computing costs, and convergence monitoring. This article describes the parallelization
of a reparametrized and marginalized posterior sampling (RAMPS) algorithm, which is carefully designed to generate posterior
samples efficiently. The algorithm is implemented using the Parallel Linear Algebra Package (PLAPACK). The scalability of
the algorithm is investigated via simulation experiments that are implemented using a cluster with 25 processors. The usefulness
of the method is illustrated with an application to sulfur dioxide concentration data from the Air Quality System database
of the U.S. Environmental Protection Agency. 相似文献
5.
Bayesian models for relative archaeological chronology building 总被引:1,自引:0,他引:1
Caitlin E. Buck & Sujit K. Sahu 《Journal of the Royal Statistical Society. Series C, Applied statistics》2000,49(4):423-440
For many years, archaeologists have postulated that the numbers of various artefact types found within excavated features should give insight about their relative dates of deposition even when stratigraphic information is not present. A typical data set used in such studies can be reported as a cross-classification table (often called an abundance matrix or, equivalently, a contingency table) of excavated features against artefact types. Each entry of the table represents the number of a particular artefact type found in a particular archaeological feature. Methodologies for attempting to identify temporal sequences on the basis of such data are commonly referred to as seriation techniques. Several different procedures for seriation including both parametric and non-parametric statistics have been used in an attempt to reconstruct relative chronological orders on the basis of such contingency tables. We develop some possible model-based approaches that might be used to aid in relative, archaeological chronology building. We use the recently developed Markov chain Monte Carlo method based on Langevin diffusions to fit some of the models proposed. Predictive Bayesian model choice techniques are then employed to ascertain which of the models that we develop are most plausible. We analyse two data sets taken from the literature on archaeological seriation. 相似文献
6.
Binary probability maps using a hidden conditional autoregressive Gaussian process with an application to Finnish common toad data 总被引:3,自引:0,他引:3
I. S. Weir & A. N. Pettitt 《Journal of the Royal Statistical Society. Series C, Applied statistics》2000,49(4):473-484
The Finnish common toad data of Heikkinen and Hogmander are reanalysed using an alternative fully Bayesian model that does not require a pseudolikelihood approximation and an alternative prior distribution for the true presence or absence status of toads in each 10 km×10 km square. Markov chain Monte Carlo methods are used to obtain posterior probability estimates of the square-specific presences of the common toad and these are presented as a map. The results are different from those of Heikkinen and Hogmander and we offer an explanation in terms of the prior used for square-specific presence of the toads. We suggest that our approach is more faithful to the data and avoids unnecessary confounding of effects. We demonstrate how to extend our model efficiently with square-specific covariates and illustrate this by introducing deterministic spatial changes. 相似文献
7.
Walter R. Gilks & Carlo Berzuini 《Journal of the Royal Statistical Society. Series B, Statistical methodology》2001,63(1):127-146
Markov chain Monte Carlo (MCMC) sampling is a numerically intensive simulation technique which has greatly improved the practicality of Bayesian inference and prediction. However, MCMC sampling is too slow to be of practical use in problems involving a large number of posterior (target) distributions, as in dynamic modelling and predictive model selection. Alternative simulation techniques for tracking moving target distributions, known as particle filters, which combine importance sampling, importance resampling and MCMC sampling, tend to suffer from a progressive degeneration as the target sequence evolves. We propose a new technique, based on these same simulation methodologies, which does not suffer from this progressive degeneration. 相似文献
8.
Efthymios G. Tsonias 《Statistical Papers》2000,41(3):305-325
The paper proposes a Markov Chain Monte Carlo method for Bayesian analysis of general regression models with disturbances from the family of stable distributions with arbitrary characteristic exponent and skewness parameter. The method does not require data augmentation and is based on combining fast Fourier transforms of the characteristic function to get the likelihood function and a Metropolis random walk chain to perform posterior analysis. Both a validation nonlinear regression and a nonlinear model for the Standard and Poor’s composite price index illustrate the method. 相似文献
9.
Generalized linear models with random effects and/or serial dependence are commonly used to analyze longitudinal data. However, the computation and interpretation of marginal covariate effects can be difficult. This led Heagerty (1999, 2002) to propose models for longitudinal binary data in which a logistic regression is first used to explain the average marginal response. The model is then completed by introducing a conditional regression that allows for the longitudinal, within‐subject, dependence, either via random effects or regressing on previous responses. In this paper, the authors extend the work of Heagerty to handle multivariate longitudinal binary response data using a triple of regression models that directly model the marginal mean response while taking into account dependence across time and across responses. Markov Chain Monte Carlo methods are used for inference. Data from the Iowa Youth and Families Project are used to illustrate the methods. 相似文献
10.
Steven X. Wei 《Econometric Reviews》2013,32(4):417-439
This paper develops a posterior simulation method for a dynamic Tobit model. The major obstacle rooted in such a problem lies in high dimensional integrals, induced by dependence among censored observations, in the likelihood function. The primary contribution of this study is to develop a practical and efficient sampling scheme for the conditional posterior distributions of the censored (i.e., unobserved) data, so that the Gibbs sampler with the data augmentation algorithm is successfully applied. The substantial differences between this approach and some existing methods are highlighted. The proposed simulation method is investigated by means of a Monte Carlo study and applied to a regression model of Japanese exports of passenger cars to the U.S. subject to a non-tariff trade barrier. 相似文献
11.
A bayesian approach to dynamic tobit models 总被引:1,自引:0,他引:1
Steven X. Wei 《Econometric Reviews》1999,18(4):417-439
This paper develops a posterior simulation method for a dynamic Tobit model. The major obstacle rooted in such a problem lies in high dimensional integrals, induced by dependence among censored observations, in the likelihood function. The primary contribution of this study is to develop a practical and efficient sampling scheme for the conditional posterior distributions of the censored (i.e., unobserved) data, so that the Gibbs sampler with the data augmentation algorithm is successfully applied. The substantial differences between this approach and some existing methods are highlighted. The proposed simulation method is investigated by means of a Monte Carlo study and applied to a regression model of Japanese exports of passenger cars to the U.S. subject to a non-tariff trade barrier. 相似文献
12.
《Journal of Statistical Computation and Simulation》2012,82(6):1247-1263
Based on the Bayesian framework of utilizing a Gaussian prior for the univariate nonparametric link function and an asymmetric Laplace distribution (ALD) for the residuals, we develop a Bayesian treatment for the Tobit quantile single-index regression model (TQSIM). With the location-scale mixture representation of the ALD, the posterior inferences of the latent variables and other parameters are achieved via the Markov Chain Monte Carlo computation method. TQSIM broadens the scope of applicability of the Tobit models by accommodating nonlinearity in the data. The proposed method is illustrated by two simulation examples and a labour supply dataset. 相似文献
13.
David Hirst Sondre Aanes Geir Storvik Ragnar Bang Huseby Ingunn Fride Tvete 《Journal of the Royal Statistical Society. Series C, Applied statistics》2004,53(1):1-14
Summary. The paper develops a Bayesian hierarchical model for estimating the catch at age of cod landed in Norway. The model includes covariate effects such as season and gear, and can also account for the within-boat correlation. The hierarchical structure allows us to account properly for the uncertainty in the estimates. 相似文献
14.
E. M. Conlon B. L. Postier B. A. Methé K. P. Nevin D. R. Lovley 《Journal of applied statistics》2009,36(10):1067-1085
The development of new technologies to measure gene expression has been calling for statistical methods to integrate findings across multiple-platform studies. A common goal of microarray analysis is to identify genes with differential expression between two conditions, such as treatment versus control. Here, we introduce a hierarchical Bayesian meta-analysis model to pool gene expression studies from different microarray platforms: spotted DNA arrays and short oligonucleotide arrays. The studies have different array design layouts, each with multiple sources of data replication, including repeated experiments, slides and probes. Our model produces the gene-specific posterior probability of differential expression, which is the basis for inference. In simulations combining two and five independent studies, our meta-analysis model outperformed separate analyses for three commonly used comparison measures; it also showed improved receiver operating characteristic curves. When combining spotted DNA and CombiMatrix short oligonucleotide array studies of Geobacter sulfurreducens, our meta-analysis model discovered more genes for fixed thresholds of posterior probability of differential expression and Bayesian false discovery than individual study analyses. We also examine an alternative model and compare models using the deviance information criterion. 相似文献
15.
C. P. Robert T. Rydén & D. M. Titterington 《Journal of the Royal Statistical Society. Series B, Statistical methodology》2000,62(1):57-75
Hidden Markov models form an extension of mixture models which provides a flexible class of models exhibiting dependence and a possibly large degree of variability. We show how reversible jump Markov chain Monte Carlo techniques can be used to estimate the parameters as well as the number of components of a hidden Markov model in a Bayesian framework. We employ a mixture of zero-mean normal distributions as our main example and apply this model to three sets of data from finance, meteorology and geomagnetism. 相似文献
16.
The last decade has witnessed major developments in Geographical Information Systems (GIS) technology resulting in the need for statisticians to develop models that account for spatial clustering and variation. In public health settings, epidemiologists and health-care professionals are interested in discerning spatial patterns in survival data that might exist among the counties. This paper develops a Bayesian hierarchical model for capturing spatial heterogeneity within the framework of proportional odds. This is deemed more appropriate when a substantial percentage of subjects enjoy prolonged survival. We discuss the implementation issues of our models, perform comparisons among competing models and illustrate with data from the SEER (Surveillance Epidemiology and End Results) database of the National Cancer Institute, paying particular attention to the underlying spatial story. 相似文献
17.
Georgiana Onicescu Andrew B. Lawson Jiajia Zhang Mulugeta Gebregziabher Kristin Wallace Jan M. Eberth 《Journal of applied statistics》2018,45(3):568-585
In this paper we propose a novel Bayesian statistical methodology for spatial survival data. Our methodology broadens the definition of the survival, density and hazard functions by explicitly modeling the spatial dependency using direct derivations of these functions and their marginals and conditionals. We also derive spatially dependent likelihood functions. Finally we examine the applications of these derivations with geographically augmented survival distributions in the context of the Louisiana Surveillance, Epidemiology, and End Results registry prostate cancer data. 相似文献
18.
In this paper we present a review of population-based simulation for static inference problems. Such methods can be described as generating a collection of random variables {X
n
}
n=1,…,N
in parallel in order to simulate from some target density π (or potentially sequence of target densities). Population-based simulation is important as many challenging sampling problems
in applied statistics cannot be dealt with successfully by conventional Markov chain Monte Carlo (MCMC) methods. We summarize
population-based MCMC (Geyer, Computing Science and Statistics: The 23rd Symposium on the Interface, pp. 156–163, 1991; Liang and Wong, J. Am. Stat. Assoc. 96, 653–666, 2001) and sequential Monte Carlo samplers (SMC) (Del Moral, Doucet and Jasra, J. Roy. Stat. Soc. Ser. B 68, 411–436, 2006a), providing a comparison of the approaches. We give numerical examples from Bayesian mixture modelling (Richardson and Green,
J. Roy. Stat. Soc. Ser. B 59, 731–792, 1997). 相似文献
19.
K. Hemming J. E. H. Shaw 《Journal of the Royal Statistical Society. Series C, Applied statistics》2002,51(4):421-435
Summary. Much current analysis of cancer registry data uses the semiparametric proportional hazards Cox model. In this paper, the time-dependent effect of various prognostic indicators on breast cancer survival times from the West Midlands Cancer Intelligence Unit are investigated. Using Bayesian methodology and Markov chain Monte Carlo estimation methods, we develop a parametric dynamic survival model which avoids the proportional hazards assumption. The model has close links to that developed by both Gamerman and Sinha and co-workers: the log-base-line hazard and covariate effects are piecewise constant functions, related between intervals by a simple stochastic evolution process. Here this evolution is assigned a parametric distribution, with a variance that is further included as a hyperparameter. To avoid problems of convergence within the Gibbs sampler, we consider using a reparameterization. It is found that, for some of the prognostic indicators considered, the estimated effects change with increasing follow-up time. In general those prognostic indicators which are thought to be representative of the most hazardous groups (late-staged tumour and oldest age group) have a declining effect. 相似文献
20.
In electrical tomography, multiple measurements of voltage are taken between electrodes on the boundary of a region with the
aim of investigating the electrical conductivity distribution within the region. The relationship between conductivity and
voltage is governed by an elliptic partial differential equation derived from Maxwell’s equations. Recent statistical approaches,
combining Bayesian methods with Markov chain Monte Carlo (MCMC) algorithms, allow to greater flexibility than classical inverse
solution approaches and require only the calculation of voltages from a conductivity distribution. However, solution of this
forward problem still requires the use of the Finite Difference Method (FDM) or the Finite Element Method (FEM) and many thousands
of forward solutions are needed which strains practical feasibility.
Many tomographic applications involve locating the perimeter of some homogeneous conductivity objects embedded in a homogeneous
background. It is possible to exploit this type of structure using the Boundary Element Method (BEM) to provide a computationally
efficient alternative forward solution technique. A geometric model is then used to define the region boundary, with priors
on boundary smoothness and on the range of feasible conductivity values. This paper investigates the use of a BEM/MCMC approach
for electrical resistance tomography (ERT) data. The efficiency of the boundary-element method coupled with the flexibility
of the MCMC technique gives a promising new approach to object identification in electrical tomography. Simulated ERT data
are used to illustrate the procedures. 相似文献