Reparameterizing the generalized linear model to accelerate gibbs sampler convergence

Nandram, Balgobin; Chen, Ming‐Hui

doi:10.1080/00949659608811724

Cited by 86 publications

(76 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Specifically, since our consequentiality responses are ordinal, our model must contend with the estimation of cutpoint values. It is well-documented in the literature that standard Gibbs sampling schemes in such models can suffer from very poor mixing, particularly in moderately large data sets, thus producing imprecise and potentially inaccurate posterior inference [e.g., Cowles (1996), Nandram and Chen (1996)]. 3 Our proposed posterior simulator offers significant improvements by sampling the cutpoints, latent willingness to pay and latent consequentiality variables in a single step rather than sampling each component from its corresponding complete posterior conditional distribution.…”

Section: Introductionmentioning

confidence: 99%

What are the consequences of consequentiality?

Herriges

Kling

Liu

et al. 2010

Journal of Environmental Economics and Management

150

101

View full text Add to dashboard Cite

We offer an empirical test of a theoretical result in the contingent valuation literature. Specifically, it has been argued from a theoretical point of view that survey participants who perceive a survey to be "consequential" will respond to questions truthfully regardless of the degree of perceived consequentiality. Using survey data from the Iowa Lakes Project, we test this supposition. Specifically, we employ a Bayesian treatment effect model in which the degree of perceived consequentiality, measured as an ordinal response, is permitted to have a structural impact on willingness to pay (WTP) for a hypothetical environmental improvement. We test our theory by determining if the WTP distributions are the same for each value of the ordinal response.In our survey data, a subsample of individuals were randomly assigned supporting information suggesting that their responses to the questionnaires were important and will have an impact on policy decisions. In conjunction with a Bayesian posterior simulator, we use this source of exogenous variation to identify the structural impacts of consequentiality perceptions on willingness to pay, while controlling for the potential of confounding on unobservables. We find evidence consistent with the "knife-edge" theoretical results, namely that the willingness to pay distributions are equal among those believing the survey to be at least minimally consequential, and different for those believing that the survey is irrelevant for policy purposes.

show abstract

Section: Introductionmentioning

confidence: 99%

What are the consequences of consequentiality?

Herriges

Kling

Liu

et al. 2010

Journal of Environmental Economics and Management

150

101

View full text Add to dashboard Cite

show abstract

“…Furthermore, following Nandram and Chen (1996) and Chen and Dey (2000), we assume that γ C−1 = 1; this is in addition to Chen and Dey (2000) propose the following transformation of cutoff points:…”

Section: Bayesian Ordered Probit Modelmentioning

confidence: 99%

“…Nandram and Chen (1996) and Chen and Dey (2000) provide an identification restriction based on the reparameterization of the cutoff points, that is, γ C−1 = 1. See also Jeliazkov et al (2009, Section 2.2).…”

Section: Bayesian Ordered Probit Modelmentioning

confidence: 99%

“…The algorithm drawing the parameters in the ordered probit model is based on the algorithms of Nandram and Chen (1996) and Chen and Dey (2000) and is as follows.…”

Section: Bayesian Ordered Probit Modelmentioning

confidence: 99%

“…9 The article proceeds as follows. In Section 2, we describe the Bayesian ordered probit model and its estimation procedure by using the algorithm in Nandram and Chen (1996) and Chen and Dey (2000). One merit of Bayesian analysis is that we can obtain the values of latent variables directly from the posterior results.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Measuring inequality of subjective well-being: A Bayesian approach

Hasegawa

Ueda

2011

The Journal of Socio-Economics

View full text Add to dashboard Cite

This article proposes a new measure for inequality of subjective wellbeing and shows the empirical results using a Bayesian ordered probit model. We introduce a new concept called "regret" as a measure for inequality of subjective well-being. Regret is the probability with which a respondent who chooses an option in a multiple-choice question pertaining to subjective well-being does not choose any other option indicative of better well-being. Regret is estimated in connection with demographic factors using the Markov chain Monte Carlo (MCMC) method and data of the World Values Survey. Furthermore, the relationships between regret and GDP per capita and the changes therein are shown to investigate those between inequality of subjective well-being and economic conditions.

show abstract

Applying Bayesian ideas to the development of medical guidelines

Landrum

Normand

1999

Statist. Med.

View full text Add to dashboard Cite

Measurements of the quality of health care, in particular the underuse and overuse of medical therapies and diagnostic tests, often involve employment of medical practice guidelines to assess the appropriateness of treatments. This paper presents a case study of a Bayesian analysis for the development of medical guidelines based on expert opinion, using ordinal categorical rater data. We develop guidelines for the use of coronary angiography following an acute myocardial infarction (AMI) for 890 clinical indications using statistical models fit to appropriateness ratings obtained from a nine-member expert panel. The main foci of our analyses were on the estimation of an appropriateness score for each of the clinical indications, an associated measure of precision, and functions of the underlying score. We considered two classes of models that assume the ratings are either in the form of grouped normal data or are ungrouped variables arising from a normal distribution, while permitting rater effects and indication heterogeneity in both. We estimated models using Markov chain Monte Carlo methods and constructed indices quantifying appropriateness based on posterior probabilities of selected model parameters. We compared our model-based approach to the standard approach currently employed in medical guideline development and found that the standard approach correctly identified 99 per cent of the appropriate indications while overestimating appropriateness 18 per cent of the time compared to our model-based approach.

show abstract

Reparameterizing the generalized linear model to accelerate gibbs sampler convergence

Cited by 86 publications

References 8 publications

What are the consequences of consequentiality?

What are the consequences of consequentiality?

Measuring inequality of subjective well-being: A Bayesian approach

Applying Bayesian ideas to the development of medical guidelines

Contact Info

Product

Resources

About