Restricted spatial regression in practice: geostatistical models, confounding, and robustness under model misspecification

2015 ◽  
Vol 26 (4) ◽  
pp. 243-254 ◽  
Author(s):  
Ephraim M. Hanks ◽  
Erin M. Schliep ◽  
Mevin B. Hooten ◽  
Jennifer A. Hoeting
Keyword(s):  
Spatial Regression ◽  
Model Misspecification ◽  
Geostatistical Models ◽  
Restricted Spatial Regression

scholarly journals Bayesian restricted spatial regression for examining session features and patient outcomes in open-enrollment group therapy studies

2015 ◽  
Vol 35 (1) ◽  
pp. 97-114 ◽  
Author(s):  
Susan M. Paddock ◽  
Thomas J. Leininger ◽  
Sarah B. Hunter
Keyword(s):  
Group Therapy ◽  
Patient Outcomes ◽  
Spatial Regression ◽  
Open Enrollment ◽  
Therapy Studies ◽  
Restricted Spatial Regression

Spatiotemporal clustering, social inequities and the risk of leptospirosis in an endemic area of Brazil: a retrospective spatial modelling

2020 ◽  
Author(s):  
Katyucia O C de Souza ◽  
José Augusto P Góes ◽  
Matheus S Melo ◽  
Paula M G Leite ◽  
Lucas A Andrade ◽  
...  
Keyword(s):  
Urban Areas ◽  
Spatial Regression ◽  
Temporal Trends ◽  
Spatial Dynamics ◽  
Secondary Data ◽  
The State ◽  
Scan Statistics ◽  
Northeastern Brazil ◽  
Metropolitan Region ◽  
Social Inequities

Abstract Background Leptospirosis is an endemic disease in Brazil of social and economic relevance related to behavioural and socioenvironmental factors. This study aimed to analyse the spatiotemporal distribution of the incidence of leptospirosis and its association with social determinants in health in a state of northeastern Brazil. Methods An ecological study of temporal series with techniques of spatial analysis using secondary data of the cases of leptospirosis notified in the Information System of Notifiable Diseases of the state of Sergipe (2008–2017) was conducted. The analysis of temporal trends was performed using Poisson regression. Spatial analyses were performed using the Moran index, the local empirical Bayesian model, scan statistics and spatial regression. Results The incidence rate decreased from 3.66 to 1.44 cases per 100 000 inhabitants in 2008 and 2017, respectively. Leptospirosis was associated with social inequities, mostly affecting males aged 20–49 y living in urban areas. The space-time scan indicated the formation of a risk cluster in municipalities in the metropolitan region of the state. Conclusions The data indicated the persistence of leptospirosis transmission, maintaining a pattern of high endemicity in some municipalities associated with social inequities. The study showed the temporal and spatial dynamics of the disease to better target specific actions for prevention and control.

scholarly journals The Impacts of the Geographic Distribution of Manufacturing Plants on Groundwater Withdrawal in China

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1158
Author(s):  
Yanting Zheng ◽  
Huidan Yang ◽  
Jinyuan Huang ◽  
Linjuan Wang ◽  
Aifeng Lv
Keyword(s):  
Manufacturing Industry ◽  
Tap Water ◽  
Demand Management ◽  
Spatial Regression ◽  
High Water ◽  
Groundwater Withdrawal ◽  
Ecological Environment ◽  
Manufacturing Plants ◽  
Water Users ◽  
Water Transmission

The overexploitation of groundwater in China has raised concern, as it has caused a series of environmental and ecological problems. However, far too little attention has been paid to the relationship between groundwater use and the spatial distribution of water users, especially that of manufacturing factories. In this study, a factory scatter index (FSI) was constructed to represent the spatial dispersion degree of manufacturing factories in China. It was found that counties and border areas between neighboring provinces registered the highest FSI increases. Further non-spatial and spatial regression models using 205 provincial-level secondary river basins in China from 2016 showed that the scattered distribution of manufacturing plants played a key role in groundwater withdrawal in China, especially in areas with a fragile ecological environment. The scattered distribution of manufacturing plants raises the cost of tap water transmission, makes monitoring and supervision more difficult, and increases the possibility of surface water pollution, thereby intensifying groundwater withdrawal. A reasonable spatial adjustment of manufacturing industry through planning and management can reduce groundwater withdrawal and realize the protection of groundwater. Our study may provide a basis for water-demand management through spatial adjustment in areas with high water scarcity and a fragile ecological environment.

scholarly journals Use of the Lagrange Multiplier Test for Assessing Measurement Invariance Under Model Misspecification

2021 ◽  
pp. 001316442110203
Author(s):  
Lucia Guastadisegni ◽  
Silvia Cagnone ◽  
Irini Moustaki ◽  
Vassilis Vasdekis
Keyword(s):  
Lagrange Multiplier ◽  
False Positive ◽  
Latent Variable ◽  
Model Misspecification ◽  
Cross Product ◽  
Small Sample ◽  
Type I ◽  
Local Dependence ◽  
Lagrange Multiplier Test ◽  
Product Approach

This article studies the Type I error, false positive rates, and power of four versions of the Lagrange multiplier test to detect measurement noninvariance in item response theory (IRT) models for binary data under model misspecification. The tests considered are the Lagrange multiplier test computed with the Hessian and cross-product approach, the generalized Lagrange multiplier test and the generalized jackknife score test. The two model misspecifications are those of local dependence among items and nonnormal distribution of the latent variable. The power of the tests is computed in two ways, empirically through Monte Carlo simulation methods and asymptotically, using the asymptotic distribution of each test under the alternative hypothesis. The performance of these tests is evaluated by means of a simulation study. The results highlight that, under mild model misspecification, all tests have good performance while, under strong model misspecification, the tests performance deteriorates, especially for false positive rates under local dependence and power for small sample size under misspecification of the latent variable distribution. In general, the Lagrange multiplier test computed with the Hessian approach and the generalized Lagrange multiplier test have better performance in terms of false positive rates while the Lagrange multiplier test computed with the cross-product approach has the highest power for small sample sizes. The asymptotic power turns out to be a good alternative to the classic empirical power because it is less time consuming. The Lagrange tests studied here have been also applied to a real data set.

scholarly journals Measuring Highway Impacts on House Prices Using Spatial Regression

2015 ◽  
Vol 7 (1) ◽  
pp. 83-98
Author(s):  
Marcus T. Allen ◽  
Grant W. Austin ◽  
Mushfiq Swaleheen
Keyword(s):  
House Prices ◽  
Spatial Regression

scholarly journals Spatial analysis for political scientists

2021 ◽  
pp. 1-17
Author(s):  
Jessica Di Salvatore ◽  
Andrea Ruggeri
Keyword(s):  
Comparative Politics ◽  
Spatial Data ◽  
Spatial Clustering ◽  
Statistical Tests ◽  
Spatial Regression ◽  
Spatial Models ◽  
Building Blocks ◽  
Spatial Relationships ◽  
Electoral Studies ◽  
Data Points

Abstract How does space matter in our analyses? How can we evaluate diffusion of phenomena or interdependence among units? How biased can our analysis be if we do not consider spatial relationships? All the above questions are critical theoretical and empirical issues for political scientists belonging to several subfields from Electoral Studies to Comparative Politics, and also for International Relations. In this special issue on methods, our paper introduces political scientists to conceptualizing interdependence between units and how to empirically model these interdependencies using spatial regression. First, the paper presents the building blocks of any feature of spatial data (points, polygons, and raster) and the task of georeferencing. Second, the paper discusses what a spatial matrix (W) is, its varieties and the assumptions we make when choosing one. Third, the paper introduces how to investigate spatial clustering through visualizations (e.g. maps) as well as statistical tests (e.g. Moran's index). Fourth and finally, the paper explains how to model spatial relationships that are of substantive interest to some of our research questions. We conclude by inviting researchers to carefully consider space in their analysis and to reflect on the need, or the lack thereof, to use spatial models.

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