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Lawson A.B., Browne W.J., Rodeiro C.L. — Disease Mapping with WinBUGS and MLwiN
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Название: Disease Mapping with WinBUGS and MLwiN
Авторы: Lawson A.B., Browne W.J., Rodeiro C.L.
Аннотация: Disease mapping involves the analysis of geo-referenced disease incidence data and has many applications, for example within resource allocation, cluster alarm analysis, and ecological studies. There is a real need amongst public health workers for simpler and more efficient tools for the analysis of geo-referenced disease incidence data. Bayesian and multilevel methods provide the required efficiency, and with the emergence of software packages – such as WinBUGS and MLwiN – are now easy to implement in practice. Provides an introduction to Bayesian and multilevel modelling in disease mapping. Adopts a practical approach, with many detailed worked examples. Includes introductory material on WinBUGS and MLwiN. Discusses three applications in detail – relative risk estimation, focused clustering, and ecological analysis. Suitable for public health workers and epidemiologists with a sound statistical knowledge. Supported by a Website featuring data sets and WinBUGS and MLwiN programs. Disease Mapping with WinBUGS and MLwiN provides a practical introduction to the use of software for disease mapping for researchers, practitioners and graduate students from statistics, public health and epidemiology who analyse disease incidence data.
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Рубрика: Математика /Вероятность /Статистика и приложения /
Статус предметного указателя: Готов указатель с номерами страниц
ed2k: ed2k stats
Год издания: 2003
Количество страниц: 282
Добавлена в каталог: 04.06.2005
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Предметный указатель
Adaptive rejection sampling 24 41 47 96—97
At risk background 4
Autocorrelation function ACF 99
BACC 23
Bayes estimate 11
Bayes factor 26
Bayesian information criterion 26
Bayesian residual 27
Block kriging 210
Block updates 23
Boa 66
Bootstrapping 75
Brooks — Draper diagnostic 25 99
BYM model 123 125
Case control 7
Case-control design 158
Case-event analysis 2
Census tracts 2
Cluster detection 1
Coda 63 66
compound documents 48
Conditional autoregressive models, CAR 43 76 111 123 222
Conditional autoregressive models, car.I1 70
Conditional autoregressive models, car.normal 70
Conditional autoregressive models, car.proper 70 124
Conditional predictive ordinates 28
Confounder variables 6 7
Constant nodes 51
Control disease 158
Convergence 66
Convolution model 111
Correlated heterogeneity 10
Correlated heterogeneity, CH 167
Counties 2
Cross-validation 208
Cusum methods 2 5
DAG, directed acyclic graph 19
Dataset, East German lip cancer, 1980—1989 207
Dataset, European melanoma mortality, 1971—1980 77
Dataset, Falkirk respiratory cancer, 1978—1983 167 190—191
Dataset, Ohio respiratory cancer mortality, 1979—1988 116 128 180
Dataset, owa cancer registry survival data 235
Dataset, Scottish lip cancer 105
Dataset, South Carolina cancer mortality, 1998 69
Dataset, South Carolina congenital abnormality deaths 5 116 213
Dataset, South Carolina low birth weight, 2000 203 223
Dataset, South Carolina malignant neoplasm mortality, 1999 116 199 220
Dataset, total deaths in England and Wales 31
Deprivation indices 7
Deviance 62
DIC diagnostic 38 100 126 1 227 233
DIC diagnostic, deviance information criterion 26 47
Diffuse prior distributions 3 7
Disease clustering 1
Disease mapping 1
Doodlebugs 49 59
Ecological analysis 1 160
Ecological bias 197
Ecological fallacy 165
Ecological regression studies 199
EDA methods 6
Edge effects 251
EM algorithm 36
Empirical Bayes 11
Excel 77
Exposure modelling 160
External standardization 7
Extra binomial variation 227
Frailty 9 181
Frequentist approach 10
Full Bayes 11
Gauss — Hermite quadrature 15 39
Gaussian kriging models 71
Gelman — Rubin diagnostic 119 122 124 132 199
Gelman — Rubin statistic 25 63 130
Generalized additive models 6
Generalized linear mixed models 45
GeoBUGS 67
Geographically weighted regression GWR 207 232
Gibbs sampling 13 45
Gibbs Updates 23 96
Gibbs — Metropolis sampling 14
GIS, geographical information systems xvii
Glm function 9
GLS estimation 36
Graphical models 51
Hidden process models 252
Hierarchical models 18
HLM package 30 37
Hybrid Metropolis — Gibbs algorithm 41
Hyperparameters 123
Hyperprior distribution 18 123
Intra-class correlation 31
Inverse Wishart prior distribution 152
Iterative generalized least squares estimation, IGLSE 36 109
Kernel regression 6
Laplace approximation 40
Laplace asymptotic integral approximation 13
Likelihood models 6
Linear Bayes methods 13
Log-concave posterior 24
Log-linear modelling 9
Log-normal model 120
Logical nodes 51
Map surveillance 252
Marginal likelihood 11
Marginal maximum likelihood 11
Marginal quasi-likelihood (MOL) algorithm 40
Markov chain 20—21
Markov chain Monte Carlo 27 46 94
Maximum a posteriori estimation 12 20
MCMC convergence 25
MCMC diagnostics 104
Metropolis updates 22 96
Metropolis — Hastings algorithm 14 21—22
Metropolis — Hastings updates 22
Mixture model 133
MLwiN macros 1 50
MLwiN worksheet 77
MOL estimation 87 90 107 146
Moran's I statistic 27
Multiple comparisons 158
Multiple-membership models 15 42 106 222
Multiple-membership multiple-classification models MMMC 43
Multiplicative expected risk 4
Multivariate normal distribution 3 6
Multivariate, CAR models 252
Multivariate, disease mapping 251
Nearest neighbour sets 216
Nonparametric maximum likelihood 13
Nonspecific heterogeneity 10
Overdispersion 9
Parametric bootstrap 2 7
Partial autocorrelation function, PACF 99
Partition models 6
Point process models 2 252
Poisson distribution 7
Poisson process models 6 252
Poisson regression 8 85—86 101
Poisson-gamma model 11 117 118
POL algorithm 40 90 97 108
Post code 2
Post hoc analysis 158
Posterior approximation 11
Posterior distribution 11 17
Posterior expectation 11
Posterior inference 19
Posterior predictive distribution 28
Posterior sampling 12
Prior distributions 17
Prospective study 156
Public health surveillance 1
Putative source of hazard 1
Quantile-quantile plots 25
Quasi-likelihood estimates 40
Quasi-likelihood methods 43 98 231
Raftery — Lewis diagnostic 25 99
Random coefficient model 33
Random effects 9 11
Random intercepts model 33
Random slopes model 33
Random walk 181
Rectangular format 5 7
Relative risk 1 4 8
Restricted iterative generalized least squares, RIGLS 36
Restricted maximum likelihood, REML 36 75
Retrospective study 156
S-Plus format 57
Saturated estimate 5
Saturated ML estimator 7
Scripts 64
Sequential Monte Carlo estimation 252
Simple kriging 214
Slice sampling 24
Space-time interaction 185
Space-time models 128
Sparseness 9
Spatial autocorrelation 10 15
Spatial prediction 137
Spatial survival analysis 235
Spatial trend 7
Specific heterogeneity 10
Standardized mortality/morbidity ratio, SMR 5 31 116
Standardized mortality/morbidity ratio, variance 5
Stochastic nodes 51
Tract centroid 2 4
Tract count analysis 2
Uncorrelated heterogeneity, UH 10 167
Univariate random walk Metropolis sampling 41
Unobserved heterogeneity 162
Uranium field 213
VARCL 30 36
Variance components model 31
zip code 2
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