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Longitudinal data analysis using generalized linear models
SUMMARY This paper proposes an extension of generalized linear models to the analysis of longitudinal data. We introduce a class of estimating equations that give consistent estimates of the
Longitudinal data analysis for discrete and continuous outcomes.
A class of generalized estimating equations (GEEs) for the regression parameters is proposed, extensions of those used in quasi-likelihood methods which have solutions which are consistent and asymptotically Gaussian even when the time dependence is misspecified as the authors often expect.
Models for longitudinal data: a generalized estimating equation approach.
This article discusses extensions of generalized linear models for the analysis of longitudinal data in which heterogeneity in regression parameters is explicitly modelled and uses a generalized estimating equation approach to fit both classes of models for discrete and continuous outcomes.
Nonlinear Time Series : Nonparametric and Parametric Methods
Although Nonlinear Time Series is the only part of the title to appear on the spine of this new book by Fan and Yao, the word “nonparametric” in the subtitle really deserves top billing. There are
Analysis of Longitudinal Data.
Correspondence analysis is an exploratory tool for the analysis of associations between categorical variables, the results of which may be displayed graphically. For longitudinal data, two types of
Temperature and mortality in 11 cities of the eastern United States.
The authors found a strong association of the temperature-mortality relation with latitude, with a greater effect of colder temperatures on mortality risk in more-southern cities and of warmer temperatures inMore-northern cities.
Ozone and short-term mortality in 95 US urban communities, 1987-2000.
A statistically significant association between short-term changes in ozone and mortality on average for 95 large US urban communities, which include about 40% of the total US population, indicates that this widespread pollutant adversely affects public health.
Fine particulate air pollution and hospital admission for cardiovascular and respiratory diseases.
Short-term exposure to PM2.5 increases the risk for hospital admission for cardiovascular and respiratory diseases and was higher in counties located in the Eastern region of the United States, which included the Northeast, the Southeast, the Midwest, and the South.
A regression model for time series of counts
SUMMARY This paper discusses a model for regression analysis with a time series of counts. Correlation is assumed to arise from an unobservable process added to the linear predictor in a log linear
Fine particulate air pollution and mortality in 20 U.S. cities, 1987-1994.
There is consistent evidence that the levels of fine particulate matter in the air are associated with the risk of death from all causes and from cardiovascular and respiratory illnesses, and this findings strengthen the rationale for controlling the Levels of respirable particles in outdoor air.