Ecological regression

2019 ◽  
pp. 191-231
Author(s):  
Miguel A. Martinez-Beneito ◽  
Paloma Botella-Rocamora
2004 ◽  
pp. 123-143 ◽  
Author(s):  
Bernard Grofman ◽  
Samuel Merrill

Parasitology ◽  
2016 ◽  
Vol 143 (9) ◽  
pp. 1193-1203 ◽  
Author(s):  
MADHUKAR S. DAMA ◽  
LENKA MARTINEC NOVÁKOVÁ ◽  
JAROSLAV FLEGR

SUMMARYSex of the fetus is genetically determined such that an equal number of sons and daughters are born in large populations. However, the ratio of female to male births across human populations varies significantly. Many factors have been implicated in this. The theory that natural selection should favour female offspring under suboptimal environmental conditions implies that pathogens may affect secondary sex ratio (ratio of male to female births). Using regression models containing 13 potential confounding factors, we have found that variation of the secondary sex ratio can be predicted by seroprevalence of Toxoplasma across 94 populations distributed across African, American, Asian and European continents. Toxoplasma seroprevalence was the third strongest predictor of secondary sex ratio, β = −0·097, P < 0·01, after son preference, β = 0·261, P < 0·05, and fertility, β = −0·145, P < 0·001. Our preliminary results suggest that Toxoplasma gondii infection could be one of the most important environmental factors influencing the global variation of offspring sex ratio in humans. The effect of latent toxoplasmosis on public health could be much more serious than it is usually supposed to be.


2006 ◽  
Vol 2006 ◽  
pp. 1-18 ◽  
Author(s):  
D. G. Steel ◽  
M. Tranmer ◽  
D. Holt

Ecological analysis involves analysing aggregate data for groups of individuals to make inferences about relationships at the individual level. Often the results of such analyses give badly biased estimates. This paper will consider the sources of bias in linear regression analysis using aggregate data. The role of variation of the individual level relationships between groups and the consequent within-group correlations and how these are related to auxiliary variables that characterise the differences between groups is considered. A method of adjusting ecological regression for the effects of auxiliary variables is described and evaluated using data from the 1991 Australian Census.


2005 ◽  
Vol 12 (4) ◽  
pp. 397-409 ◽  
Author(s):  
Annibale Biggeri ◽  
Massimo Bonannini ◽  
Dolores Catelan ◽  
Fabio Divino ◽  
Emanuela Dreassi ◽  
...  

1986 ◽  
Vol 10 (1) ◽  
pp. 85-86
Author(s):  
Robert R. Dykstra

Those skeptical of ecological regression in voting behavior studies continue to suggest that problems in applying the technique severely limit its utility. But the cautionary offered in the Winter 1985 number of this journal by William H. Flanigan and Nancy H. Zingale (“Alchemist’s Gold: Inferring Individual Relationships from Aggregate Data,” Social Science History 9: 71-91) goes so far as to suggest that these problems are insurmountable—or virtually insurmountable. As a user, I was prepared to be devastated, but in fact find myself cheered (if a little puzzled).Interested readers will recall that the centerpiece of the authors’ argument is a test involving this question: How did the voters of 1968 behave four years later in the presidential election of 1972? The test consists of comparing voters’ actual behavior, as determined by survey data, with ecological regression estimates of that same behavior. The tabulated results are alleged to be decisive in proving the authors’ point, but instead appear to prove just the opposite of what is intended, as a fresh look at the material reveals.


2013 ◽  
Vol 28 (3) ◽  
pp. 695-706 ◽  
Author(s):  
Marc Marí-Dell’Olmo ◽  
Miguel A. Martinez-Beneito ◽  
Mercè Gotsens ◽  
Laia Palència

2019 ◽  
Vol 28 (1) ◽  
pp. 65-86
Author(s):  
Wenxin Jiang ◽  
Gary King ◽  
Allen Schmaltz ◽  
Martin A. Tanner

Ecological inference (EI) is the process of learning about individual behavior from aggregate data. We relax assumptions by allowing for “linear contextual effects,” which previous works have regarded as plausible but avoided due to nonidentification, a problem we sidestep by deriving bounds instead of point estimates. In this way, we offer a conceptual framework to improve on the Duncan–Davis bound, derived more than 65 years ago. To study the effectiveness of our approach, we collect and analyze 8,430 $2\times 2$ EI datasets with known ground truth from several sources—thus bringing considerably more data to bear on the problem than the existing dozen or so datasets available in the literature for evaluating EI estimators. For the 88% of real data sets in our collection that fit a proposed rule, our approach reduces the width of the Duncan–Davis bound, on average, by about 44%, while still capturing the true district-level parameter about 99% of the time. The remaining 12% revert to the Duncan–Davis bound.


2020 ◽  
Vol 28 (3) ◽  
pp. 2804-2809
Author(s):  
Roberto Bergamaschi ◽  
Maria Cristina Monti ◽  
Leonardo Trivelli ◽  
Giulia Mallucci ◽  
Leonardo Gerosa ◽  
...  

AbstractSome environmental factors are associated with an increased risk of multiple sclerosis (MS). Air pollution could be a main one. This study was conducted to investigate the association of particulate matter 2.5 (PM2.5) concentrations with MS prevalence in the province of Pavia, Italy. The overall MS prevalence in the province of Pavia is 169.4 per 100,000 inhabitants. Spatial ground-level PM2.5 gridded data were analysed, by municipality, for the period 2010–2016. Municipalities were grouped by tertiles according to PM2.5 concentration. Ecological regression and Bayesian statistics were used to analyse the association between PM2.5 concentrations, degree of urbanization, deprivation index and MS risk. MS risk was higher among persons living in areas with an average winter PM2.5 concentration above the European annual limit value (25 μg/m3). The Bayesian map revealed sizeable MS high-risk clusters. The study found a relationship between low MS risk and lower PM2.5 levels, strengthening the suggestion that air pollution may be one of the environmental risk factors for MS.


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