Author: Francesca DominiciPublisher:
ISBN:
Category :
Languages : en
Pages : 33
Book Description
Time-series Analysis of Air Pollution and Mortality
Author: Francesca DominiciTime Series Analysis of Mortality and Associated Weather and Pollution Effects in Los Angeles County
Author: R. H. ShumwayApplication of Principal Component Analysis to Time Series of Daily Air Pollution and Mortality
Author: C. M. QuantRegression Models for Time Series Analysis
Author: Benjamin KedemPublisher: John Wiley & Sons
ISBN: 0471461687
Category : Mathematics
Languages : en
Pages : 361
Book Description
A thorough review of the most current regression methods in time series analysis Regression methods have been an integral part of time series analysis for over a century. Recently, new developments have made major strides in such areas as non-continuous data where a linear model is not appropriate. This book introduces the reader to newer developments and more diverse regression models and methods for time series analysis. Accessible to anyone who is familiar with the basic modern concepts of statistical inference, Regression Models for Time Series Analysis provides a much-needed examination of recent statistical developments. Primary among them is the important class of models known as generalized linear models (GLM) which provides, under some conditions, a unified regression theory suitable for continuous, categorical, and count data. The authors extend GLM methodology systematically to time series where the primary and covariate data are both random and stochastically dependent. They introduce readers to various regression models developed during the last thirty years or so and summarize classical and more recent results concerning state space models. To conclude, they present a Bayesian approach to prediction and interpolation in spatial data adapted to time series that may be short and/or observed irregularly. Real data applications and further results are presented throughout by means of chapter problems and complements. Notably, the book covers: * Important recent developments in Kalman filtering, dynamic GLMs, and state-space modeling * Associated computational issues such as Markov chain, Monte Carlo, and the EM-algorithm * Prediction and interpolation * Stationary processes
Statistical Methods for Environmental Epidemiology with R
Author: Roger D. PengPublisher: Springer Science & Business Media
ISBN: 0387781676
Category : Medical
Languages : en
Pages : 151
Book Description
As an area of statistical application, environmental epidemiology and more speci cally, the estimation of health risk associated with the exposure to - vironmental agents, has led to the development of several statistical methods and software that can then be applied to other scienti c areas. The stat- tical analyses aimed at addressing questions in environmental epidemiology have the following characteristics. Often the signal-to-noise ratio in the data is low and the targets of inference are inherently small risks. These constraints typically lead to the development and use of more sophisticated (and pot- tially less transparent) statistical models and the integration of large hi- dimensional databases. New technologies and the widespread availability of powerful computing are also adding to the complexities of scienti c inves- gation by allowing researchers to t large numbers of models and search over many sets of variables. As the number of variables measured increases, so do the degrees of freedom for in uencing the association between a risk factor and an outcome of interest. We have written this book, in part, to describe our experiences developing and applying statistical methods for the estimation for air pollution health e ects. Our experience has convinced us that the application of modern s- tistical methodology in a reproducible manner can bring to bear subst- tial bene ts to policy-makers and scientists in this area. We believe that the methods described in this book are applicable to other areas of environmental epidemiology, particularly those areas involving spatial{temporal exposures.
Model Selection Applications in Time Series Studies of Air Pollution and Mortality
Author: Ying JiangPublisher:
ISBN:
Category : Air
Languages : en
Pages : 216
Book Description
A number of time series studies provide evidence that air pollution levels are associated with daily death counts. Most of these studies explain these associations by using Poisson log-linear regression models, after allowing for possible confounders. However, the findings of these time series studies suffer from several problems, of which the most significant is conflicting results from different approaches to model selection. To address this problem, this dissertation compares the statistical properties of these approaches, including those with and without model selection uncertainty. In the simulation experiments, the sensitivity of these statistical properties to the choice of bootstrap resampling schemes, including a parametric bootstrap and residual-based bootstrap, is investigated. Simulation results indicate that regardless of the type of resampling schemes used, Bayesian model averaging methods based on Akaike's Information Criterion (BMA-aic) perform well in predicting the mortality effect of particulate matter. Additionally, rather than the standard use of the Bayesian Information Criterion (BIC), this study suggests using Akaike's Information Criterion (AIC) for the data from Chicago, if prediction is the goal. Thus, when predicting seasonal mortality effects of air pollution in Chicago, this dissertation incorporates AIC to identify the pattern of unmeasured confounding effects for each season in that city. A common approach to control for unmeasured confounding effects is to adopt a natural cubic spline function of time with fixed degrees of freedom per year. This approach uses an annual adjustment for these effects. However, in reality, these effects may vary according to season; given this, seasonal adjustments for these effects are advocated. As such, this raises the issue of how to control the confounders adequately in the natural cubic spline function. To resolve this issue, a model is proposed to explain the association between particulate matter and mortality after allowing for the seasonal patterns of confounders. AIC is used to select the degrees of freedom for each season. The results indicate a high mortality level during seasons with high particulate matter concentrations.
A Time-series Analysis of Mortality and Hospital Admissions Associations with Particulate Matter Air Pollution Components in Two New York Metropolitan Areas
Author: Roberta Charon GwynnPublisher:
ISBN:
Category :
Languages : en
Pages : 502
Book Description
Mortality and Ambient Air Pollution in California, 1968-1991
Author: Steven Karl MurrayAir Pollution and Human Health
Author: Lester B. LavePublisher: Routledge
ISBN: 1135996733
Category : Technology & Engineering
Languages : en
Pages : 421
Book Description
Upon competition of a ten year research project which analyzes the effect of air pollution and death rates in US cities, Lester B. Lave and Eugene P. Seskin conclude that the mortality rate in the US could shrink by seven percent with a similar if not greater decline in disease incidence if industries followed EPA regulations in cutting back on certain pollutant emissions. The authors claim that this reduction is sufficient to add one year to average life expectancy. Originally published in 1977.
Ambient Air Pollution and Daily Mortality in North Central Texas, 1990-1994
Author: Janet Lynn GamblePublisher:
ISBN:
Category : Air
Languages : en
Pages : 330
Book Description