Titlebook: Case Studies in Bayesian Statistics; Volume VI Constantine Gatsonis,Robert E. Kass,Isabella Verdi Conference proceedings 2002 Springer-Verl

漂亮才會(huì)豪華

0930-0325 Overview: The 6th Workshop on Case Studies in Bayesian Statistics was held at the Carnegie Mellon University in October, 2001. This volume contains the invited case studies with the accompanying discussion as well as contributed papers selected by a refereeing process.978-0-387-95472-1978-1-4612-2078-7Series ISSN 0930-0325 Series E-ISSN 2197-7186

Heresy

清唱?jiǎng)?/a>

https://doi.org/10.1007/978-1-4020-8395-2response,” the patient may survive without response, “failure,” or the patient may die. When treatment fails in a given course, it is common medical practice to switch to a different treatment for the next course. Most statistical approaches to such settings simply ignore the multi-course structure.

暗語

倫理學(xué)

Two Models of School Structure,ases have in turn generated interest among statisticians to develop and analyze models that account for spatial clustering and variation. We analyze a set of spatially correlated infant mortality rates in the counties in Minnesota. In the absence of appropriate surrogates for standard of health in t

achlorhydria

Shlomo Sharan,Ivy Geok Chin Tan earlier, by solving a suitable change-point problem. We propose a hierarchical Bayesian change-point model for influenza epidemics. Prior probabilities of a change point depend on (random) factors that affect the spread of influenza. Theory of optimal stopping is used to obtain Bayes stopping rules

意見一致

archetype

Constituent

A. D. Venosa,K. Lee,Z. Li,M. C. Boufadel participated in a memory test designed to measure both spatial and object working memory. Standard analyses were inappropriate because the test items had differing levels of difficulty. To account for this problem, the data were analyzed using a Bayesian Bivariate Item Response Theory (BBIRT) model

GREG

A. D. Venosa,K. Lee,Z. Li,M. C. Boufadel. Waterborne pathogen count data serves as the basis of a Generalized Linear Mixed Model (GLMM) including covariates. The model has a hierarchical structure for sites, regions and an overall national average. Different possible models are discussed considering alternative covariates and their approp