ISSN:0162-1459    

DOI:10.1080/01621459.1996.10476911

出版商:Taylor & Francis Group    

年代:1996

数据来源: Taylor

ISSN:0162-1459    

DOI:10.1080/01621459.1996.10476912

出版商:Taylor & Francis Group    

年代:1996

数据来源: Taylor

ISSN:0162-1459    

DOI:10.1080/01621459.1996.10476913

出版商:Taylor & Francis Group    

年代:1996

数据来源: Taylor

ISSN:0162-1459    

DOI:10.1080/01621459.1996.10476914

出版商:Taylor & Francis Group    

年代:1996

数据来源: Taylor

ISSN:0162-1459    

DOI:10.1080/01621459.1996.10476915

出版商:Taylor & Francis Group    

年代:1996

数据来源: Taylor

ISSN:0162-1459    

DOI:10.1080/01621459.1996.10476916

出版商:Taylor & Francis Group    

年代:1996

数据来源: Taylor

摘要:

An underground water level data set, regularly observed over a 10-year period in the Tokai area, one of the most intensively observed areas in Japan, is analyzed. First, the 500,000+ point data set was smoothed using a non-Gaussian state-space model to interpolate missing observations and to correct outlying observations. Then the effects of barometric pressure, earth tide, and precipitation on the water level were estimated using state-space modeling. After subtracting these effects, the coseismic effects on 16 examined earthquakes are clear. The amount of the coseismic effect is explained as a function of magnitude and hypocentral distance of the earthquake. It is also shown that the underground water level had a tendency to increase approximately 6 cm/year. Presumably, this corresponds to the increase of strain in this area due to the subduction of the Philippine Sea tectonic plate under the Eurasian plate.

ISSN:0162-1459    

DOI:10.1080/01621459.1996.10476917

出版商:Taylor & Francis Group    

年代:1996

数据来源: Taylor

摘要:

Investigators of social differentials in health outcomes commonly augment incomplete microdata by appending socioeconomic characteristics of residential areas (such as median income in a zip code) to proxy for individual characteristics. But little empirical attention has been paid to how well this aggregate information serves as a proxy for the individual characteristics of interest. We build on recent work addressing the biases inherent in proxies and consider two health-related examples within a statistical framework that illuminates the nature and sources of biases. Data from the Panel Study of Income Dynamics and the National Maternal and Infant Health Survey are linked to census data. We assess the validity of using the aggregate census information as a proxy for individual information when estimating main effects and when controlling for potential confounding between socioeconomic and sociodemographic factors in measures of general health status and infant mortality. We find a general, but not universal, tendency for aggregate proxies to exaggerate the effects of micro-level variables and to do more poorly than micro-level variables at controlling for confounding. The magnitude and direction of these biases vary across samples, however. Our statistical framework and empirical findings suggest the difficulties in and limits to interpreting proxies derived from aggregate census data as if they were micro-level variables. The statistical framework that we outline for our study of health outcomes should be generally applicable to other situations where researchers have merged aggregate data with microdata samples.

ISSN:0162-1459    

DOI:10.1080/01621459.1996.10476918

出版商:Taylor & Francis Group    

年代:1996

数据来源: Taylor

摘要:

Suppose that several independent experiments are observed, each one yielding a likelihoodLk(θk) for a real-valued parameter of interest θk. For example, θkmight be the log-odds ratio for a 2 × 2 table relating to thekth population in a series of medical experiments. This article concerns the following empirical Bayes question: How can we combine all of the likelihoodsLkto get an interval estimate for any one of the θk's, say θ1? The results are presented in the form of a realistic computational scheme that allows model building and model checking in the spirit of a regression analysis. No special mathematical forms are required for the priors or the likelihoods. This scheme is designed to take advantage of recent methods that produce approximate numerical likelihoodsLk(θk) even in very complicated situations, with all nuisance parameters eliminated. The empirical Bayes likelihood theory is extended to situations where the θk's have a regression structure as well as an empirical Bayes relationship. Most of the discussion is presented in terms of a hierarchical Bayes model and concerns how such a model can be implemented without requiring large amounts of Bayesian input. Frequentist approaches, such as bias correction and robustness, play a central role in the methodology.

ISSN:0162-1459    

DOI:10.1080/01621459.1996.10476919

出版商:Taylor & Francis Group    

年代:1996

数据来源: Taylor

ISSN:0162-1459    

DOI:10.1080/01621459.1996.10476920

出版商:Taylor & Francis Group    

年代:1996

数据来源: Taylor