Comparison of regression models with land-use and emission data to ...
Recommend Documents
the spatial distribution of traffic-related air pollution in Rome. MATS ROSENLUND ... 1996 by simple univariate linear regression models. Land-use variable ...
â Agriculture and Agri-Food Canada, Dairy and Swine Research and Development Centre, ... â¡Canadian Dairy Network, Guelph, Ontario, N1K 1E5, Canada.
Central Michigan University. Abstract: Recently, count regression models have been used to model over- dispersed and zero-inflated count response variable ...
*is friction velocity) but not necessarily so in general. The dust emission predictions using the scheme are compared with several data sets published in the ...
The most popular distribution for modeling count data is the ..... C. Kleiber, S. Jackman, Regression Models for Count Data in R, Journal of Statistical Software.
During flu season, residuals were modestly lower for regression overall and at mean observed counts >20 per facility per day. Table: Mean Absolute Residuals ...
May 9, 2013 - and Walden, 1980). For the regime switching combination model in (3), minimum CRPS estimation is applied for the parameters of the TN ...
nonparametric instance-based k-nearest neighbour (k-NN) approach to estimate single-tree biomass with predictions from linear mixed-effect regression models ...
Kidney Transplantation, Kermanshah University of Medical Sciences, Kermanshah, ... artificial neural network models in prediction of kidney transplant survival.
regression coefficients, t-statistics, and correlation between log(MEDV) and the corresponding X variable. X β ... the best single predictor variable is selected to fit a linear model to MEDV. .... their values for a qualitative analysis. ..... of C
But the models, including linear regression, are typically impossible to interpret or
visualize. ..... M5 uses a hybrid strategy to build a piecewise linear model.
measures of goodness of fit for Poisson regression models are the deviance and Pearson's statistic. These two statistics are widely used by generalized linear ...
teria for building regression models robust to missing values, and a corresponding ... The goal is to build a predictive
Nov 27, 2008 - et al., 2004), Vancouver (Liu et al., 2003), Sydney (Mannes et al., .... by regression for all factors governing selection (Gelman and Carlin, 2001).
implies MAR. Let ËÏi be the estimator of the conditional probability Ïi = P(ξi = 1|Xi, Ai), and ...... The authors also thank Yuriko Nagano for some help with the data ...
Principal Component Regression (PCR) and Partial Least Squares Regres- ... The partial autocorrelation function (PACF) is one of the efficient test statistics for ...
likelihood solution is described, and available statistical software for the model is discussed. An analysis of a .... Finally, computer programs that are available for ran- ..... This en- sures that the level accounting for the largest share of tota
Nov 27, 2008 - Imperial College London, London, UK .... which we consider include birth weight, sex, ethnicity, tobacco smoking during pregnancy, maternal age, ...... air pollutants and adverse pregnancy outcomes in Vancouver, Canada.
Applied Regression Models for. Longitudinal Data. Kosuke Imai. Princeton
University. Fall 2013. POL 573 Quantitative Analysis III. Kosuke Imai (Princeton).
Jan 29, 2018 - Abstract: In the analysis of count data often the equidispersion assumption is not suit- able, hence the Poisson regression model is ...
Fig -2: Map of the Mobile - Tombigbee Basin, defined by. HUC 0316, located in ... increasing or decreasing monotonic trends of a Y-value when the X variable is ...
count data models in econometrics, including hurdle and zero-inflated models, ... objects follows that of the base R functionality so that the new software ...
Jun 3, 2009 - Bryan Lau, Stephen R. Cole, and Stephen J. Gange. Initially submitted August ...... Barkan SE, Melnick SL, Preston-Martin S, et al. The Women's.
Comparison of regression models with land-use and emission data to ...
Journal of Exposure Science and Environmental Epidemiology (2008) 18, 339; doi:10.1038/sj.jes. ... Epidemiology (2007) 17: XXXâXXX; advanced online.
Journal of Exposure Science and Environmental Epidemiology (2008) 18, 339 r 2008 Nature Publishing Group All rights reserved 1559-0631/08/$30.00
www.nature.com/jes
ERRATUM
Comparison of regression models with land-use and emission data to predict the spatial distribution of traffic-related air pollution in Rome MATS ROSENLUND, FRANCESCO FORASTIERE, MASSIMO STAFOGGIA, DANIELA PORTA, MARA PERUCCI, ANDREA RANZI, FABIO NUSSIO AND CARLO A PERUCCI
Journal of Exposure Science and Environmental Epidemiology (2008) 18, 339; doi:10.1038/sj.jes.7500603
Correction to: Journal of Exposure Science and Environmental Epidemiology (2007) 17: XXX–XXX; advanced online publication, 11 April 2007; doi:10.1038/sj.jes.7500571
Table 1 was published with errors. It is printed here correctly in its entirety. The publisher regrets for the error.
Table 1. Descriptive statistics and associations between ambient NO2 concentrations at 68 measurement sites and land-use variables in Rome 1995– 1996 by simple univariate linear regression models. Land-use variable (mean7SD) Circular traffic zones Outside the main ring roadb Between the main ring road and the green strip Between the green strip and the inner ring road Between the inner ring road and the traffic-limited zone Inside the traffic-limited zone Distance to busy roads (m) 4500b 150–500 o150 Vicinity to closest park (m) 4500b 150–500 o150 Altitude (m) (47.8724.2) Distance to the sea (km) (27.478.4) Size of the census block (ha) (9.0714.5) Number of residents by census block 1997 (662.27334.2) Inverse population density 1997 (m2) (150.27199.4) a
No. of sites
Mean NO2
SD
Slopea
14 15 30 8 1
33.9 46.0 50.0 58.8 52.0
5.6 5.1 7.0 7.9 0
F 12.1 16.1 24.9 18.1
F o0.001 o0.001 o0.001 0.008
14 30 24
38.6 47.0 51.6
6.4 8.9 9.4
F 8.6 13.2
F 0.003 o0.001
51 14 3 68 68 68 68 68
46.0 48.9 51.3 46.8 46.8 46.8 46.8 46.8
10.3 7.6 9.3 9.8 9.8 9.8 9.8 9.8
F 2.8 5.3 –0.06 0.26 –0.351 –0.008 –0.026
F 0.34 0.37 0.21 0.07 o0.001 0.02 o0.001
P
Adjusted R2 0.569
0.217
–0.0064
0.0091 0.036 0.261 0.065 0.275
The estimate derived from a simple linear regression model with only one independent variable and NO2 as the dependent variable, thus describing the increase in predicted NO2 concentration for each step on the land-use variable. For categorical variables, the first category is used as reference. b Reference category.
