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Title:

A primer on disease mapping and ecological regression using <EquationSource Format="TEX">$${\texttt{INLA}}$$</EquationSource>

Description:

Disease mapping, Ecological regression, INLA, Spatio-temporal models

Disease mapping, Ecological regression, INLA, Spatio-temporal models Minimize

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article

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Title:

Predictive assessment of a non-linear random effects model for space-time surveillance data

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Notification data collected by national surveillance systems are typically available as weekly time series of counts of confirmed new cases, stratified e.g. by geographic areas. This work outlines the statistical modeling framework in Paul and Held (2011) for the analysis of such data. Inherent (spatio-)temporal dependencies are incorporated via...

Notification data collected by national surveillance systems are typically available as weekly time series of counts of confirmed new cases, stratified e.g. by geographic areas. This work outlines the statistical modeling framework in Paul and Held (2011) for the analysis of such data. Inherent (spatio-)temporal dependencies are incorporated via an observation-driven formulation. Using region-specific and possibly spatially correlated random effects, we are able to address heterogeneous incidence levels. Inference is based on penalized likelihood methodology for mixed models. The predictive performance of models is assessed using probabilistic one-step-ahead predictions and proper scoring rules. Minimize

Year of Publication:

2011

Document Type:

conference presentation - intervento a convegno

Language:

eng

Subjects:

Time series of counts; infectious diseases; proper scoring rules; ING-IND/09 - Sistemi per l'Energia e L'Ambiente

Time series of counts; infectious diseases; proper scoring rules; ING-IND/09 - Sistemi per l'Energia e L'Ambiente Minimize

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open

open Minimize

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Spatial2: Spatial Data Methods for Environmental and Ecological processes. Proceedings.

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Title:

Bayesian Age-Period-Cohort Modeling and Prediction - BAMP

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The software package BAMP provides a method of analyzing incidence or mortality data on the Lexis diagram, using a Bayesian version of an age-period-cohort model. A hierarchical model is assumed with a binomial model in the first-stage. As smoothing priors for the age, period and cohort parameters random walks of first and second order, with and...

The software package BAMP provides a method of analyzing incidence or mortality data on the Lexis diagram, using a Bayesian version of an age-period-cohort model. A hierarchical model is assumed with a binomial model in the first-stage. As smoothing priors for the age, period and cohort parameters random walks of first and second order, with and without an additional unstructured component are available. Unstructured heterogeneity can also be included in the model. In order to evaluate the model fit, posterior deviance, DIC and predictive deviances are computed. By projecting the random walk prior into the future, future death rates can be predicted. Minimize

Publisher:

University of California at Los Angeles, Department of Statistics

Year of Publication:

2007-10-01T00:00:00Z

Document Type:

article

Language:

English

Subjects:

Bayesian hierarchical models ; age-period-cohort models ; prediction ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Scien...

Bayesian hierarchical models ; age-period-cohort models ; prediction ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H Minimize

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http://www.jstatsoft.org/v21/i08/paper

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Title:

which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. A nomogram for P values

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This Provisional PDF corresponds to the article as it appeared upon acceptance. Fully formatted PDF and full text (HTML) versions will be made available soon. A nomogram for P values

This Provisional PDF corresponds to the article as it appeared upon acceptance. Fully formatted PDF and full text (HTML) versions will be made available soon. A nomogram for P values Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2010-05-24

Source:

http://www.stat.columbia.edu/~cook/movabletype/mlm/ppvalues.pdf

http://www.stat.columbia.edu/~cook/movabletype/mlm/ppvalues.pdf Minimize

Document Type:

text

Language:

en

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Metadata may be used without restrictions as long as the oai identifier remains attached to it.

Metadata may be used without restrictions as long as the oai identifier remains attached to it. Minimize

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Title:

Bayesian Age-Period-Cohort Modeling and Prediction - BAMP

Description:

The software package BAMP provides a method of analyzing incidence or mortality data on the Lexis diagram, using a Bayesian version of an age-period-cohort model. A hierarchical model is assumed with a binomial model in the first-stage. As smoothing priors for the age, period and cohort parameters random walks of first and second order, with and...

The software package BAMP provides a method of analyzing incidence or mortality data on the Lexis diagram, using a Bayesian version of an age-period-cohort model. A hierarchical model is assumed with a binomial model in the first-stage. As smoothing priors for the age, period and cohort parameters random walks of first and second order, with and without an additional unstructured component are available. Unstructured heterogeneity can also be included in the model. In order to evaluate the model fit, posterior deviance, DIC and predictive deviances are computed. By projecting the random walk prior into the future, future death rates can be predicted. Minimize

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article

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Title:

Objective Bayesian Model Selection in Generalised Additive Models with Penalised Splines

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Publisher:

Figshare

Year of Publication:

2014

Document Type:

Dataset ; Dataset

Rights:

CC-BY ; http://creativecommons.org/licenses/by/3.0/us/

CC-BY ; http://creativecommons.org/licenses/by/3.0/us/ Minimize

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Title:

Towards Joint Disease Mapping

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This paper discusses and extends statistical models to jointly analyse the spatial variation of rates of several diseases with common risk factors.

This paper discusses and extends statistical models to jointly analyse the spatial variation of rates of several diseases with common risk factors. Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-04-18

Source:

http://www.math.ntnu.no/~hrue/reports/SMMRrevision.ps.gz

http://www.math.ntnu.no/~hrue/reports/SMMRrevision.ps.gz Minimize

Document Type:

text

Language:

en

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Metadata may be used without restrictions as long as the oai identifier remains attached to it.

Metadata may be used without restrictions as long as the oai identifier remains attached to it. Minimize

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Title:

Overview

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1. Motivating problem Forecasting cancer rates 2. Predictive model comparison and criticism Goals Proper scoring rules Tools for model criticism 3. Case study 4. Discussion Motivating problem II: Forecasting cancer rates I Bayesian age-period-cohort models are used increasingly to project cancer incidence and mortality rates. I Data from younger...

1. Motivating problem Forecasting cancer rates 2. Predictive model comparison and criticism Goals Proper scoring rules Tools for model criticism 3. Case study 4. Discussion Motivating problem II: Forecasting cancer rates I Bayesian age-period-cohort models are used increasingly to project cancer incidence and mortality rates. I Data from younger age groups (typically age < 30 years) for which rates are low are often excluded from the analysis. I However, a recent empirical comparison (Baker and Bray, 2005) based on data from Hungary suggests that age-specific predictions based on full data are more accurate. { Question how to quantify the accuracy of probabilistic forecasts { Forecast evaluation for model comparison Case study: Forecasting larynx cancer rates in Germany I We will investigate if the same conclusion can be drawn for mortality data on larynx cancer from Germany, 1952-1997. I To assess the predictive quality of the different models, we have predicted the mortality counts in the years 1998-2002. I For all different models, we looked at the predictions in the 12 age groups with age above 30 years. I One-step-ahead forecasts have also been calculated. Forecasting cancer rates cont. Observed (×) and fitted/predicted num-ber of deaths (per 100,000) for larynx cancer among males. Shown are posterior means and 90% pointwise credible intervals for the mean, calculated with software BAMP. Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2015-03-04

Source:

http://www.statoo.ch/gss06/presentations/Held.pdf

http://www.statoo.ch/gss06/presentations/Held.pdf Minimize

Document Type:

text

Language:

en

DDC:

310 Collections of general statistics *(computed)*

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Metadata may be used without restrictions as long as the oai identifier remains attached to it.

Metadata may be used without restrictions as long as the oai identifier remains attached to it. Minimize

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Title:

Bayesian Estimation of the Size of a Population

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We consider the following problem: estimate the size of a population marked with serial numbers after only a sample of the serial numbers has been ob-served. Its simplicity in formulation and the inviting possibilities of applica-tion make this estimation well suited for an undergraduate level probability course. Our contribution consists in a B...

We consider the following problem: estimate the size of a population marked with serial numbers after only a sample of the serial numbers has been ob-served. Its simplicity in formulation and the inviting possibilities of applica-tion make this estimation well suited for an undergraduate level probability course. Our contribution consists in a Bayesian treatment of the problem. For an improper uniform prior distribution, we show that the posterior mean and variance have nice closed form expressions and we demonstrate how to compute highest posterior density intervals. Maple and R code is provided on the authors ’ web-page to allow students to verify the theoretical results and experiment with data. Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2015-01-14

Source:

http://epub.ub.uni-muenchen.de/2094/1/paper_499.pdf

http://epub.ub.uni-muenchen.de/2094/1/paper_499.pdf Minimize

Document Type:

text

Language:

en

Subjects:

Bayesian inference ; Combinatorics ; Hypergeometric functions ; Maple ; R

Bayesian inference ; Combinatorics ; Hypergeometric functions ; Maple ; R Minimize

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Title:

RESEARCH ARTICLE Open Access

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Background: P values are the most commonly used tool to measure evidence against a hypothesis. Several attempts have been made to transform P values to minimum Bayes factors and minimum posterior probabilities of the hypothesis under consideration. However, the acceptance of such calibrations in clinical fields is low due to inexperience in inte...

Background: P values are the most commonly used tool to measure evidence against a hypothesis. Several attempts have been made to transform P values to minimum Bayes factors and minimum posterior probabilities of the hypothesis under consideration. However, the acceptance of such calibrations in clinical fields is low due to inexperience in interpreting Bayes factors and the need to specify a prior probability to derive a lower bound on the posterior probability. Methods: I propose a graphical approach which easily translates any prior probability and P value to minimum posterior probabilities. The approach allows to visually inspect the dependence of the minimum posterior probability on the prior probability of the null hypothesis. Likewise, the tool can be used to read off, for fixed posterior probability, the maximum prior probability compatible with a given P value. The maximum P value compatible with a given prior and posterior probability is also available. Results: Use of the nomogram is illustrated based on results from a randomized trial for lung cancer patients comparing a new radiotherapy technique with conventional radiotherapy. Conclusion: The graphical device proposed in this paper will enhance the understanding of P values as measures of evidence among non-specialists. Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2013-09-25

Source:

ftp://ftp.ncbi.nlm.nih.gov/pub/pmc/ac/49/BMC_Med_Res_Methodol_2010_Mar_16_10_21.tar.gz

ftp://ftp.ncbi.nlm.nih.gov/pub/pmc/ac/49/BMC_Med_Res_Methodol_2010_Mar_16_10_21.tar.gz Minimize

Document Type:

text

Language:

en

DDC:

310 Collections of general statistics *(computed)*

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Metadata may be used without restrictions as long as the oai identifier remains attached to it.

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