Journal ArticleDOI
Inference based on kernel estimates of the relative risk function in geographical epidemiology.
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TLDR
This work examines a computationally cheap alternative whereby the tolerance intervals are derived from asymptotic theory, and examines the performance of global tests of hetereogeneous risk employing statistics based on kernel risk surfaces, paying particular attention to the choice of smoothing parameters on test power.Abstract:
Kernel smoothing is a popular approach to estimating relative risk surfaces from data on the locations of cases and controls in geographical epidemiology. The interpretation of such surfaces is facilitated by plotting of tolerance contours which highlight areas where the risk is sufficiently high to reject the null hypothesis of unit relative risk. Previously it has been recommended that these tolerance intervals be calculated using Monte Carlo randomization tests. We examine a computationally cheap alternative whereby the tolerance intervals are derived from asymptotic theory. We also examine the performance of global tests of hetereogeneous risk employing statistics based on kernel risk surfaces, paying particular attention to the choice of smoothing parameters on test power.read more
Citations
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Who remembers a hot summer or a cold winter? The asymmetric effect of beliefs about global warming on perceptions of local climate conditions in the U.S.
TL;DR: The authors explored the extent to which public perceptions match climate conditions as recorded in instrumental climate data and found that subjective experiences of seasonal average temperature and precipitation during the previous winter and summer were related to recorded conditions during each season.
Journal ArticleDOI
The Huanan Seafood Wholesale Market in Wuhan was the early epicenter of the COVID-19 pandemic
Michael Worobey,J. Levy,Lorena Mariana Malpica Serrano,Alexander Crits-Christoph,Jonathan Pekar,Steve A.N. Goldstein,Angela L. Rasmussen,Moritz U. G. Kraemer,C. R. Newman,Marion Koopmans,Marc A. Suchard,Joel O. Wertheim,Philippe Lemey,David Robertson,Robert F. Garry,Edward C. Holmes,Andrew Rambaut,Kristian G. Andersen +17 more
TL;DR: These analyses indicate that the emergence of SARS-CoV-2 occurred via the live wildlife trade in China, and show that the Huanan market was the epicenter of the COVID-19 pandemic.
Journal ArticleDOI
Adaptive kernel estimation of spatial relative risk
TL;DR: This paper examines the properties of the adaptive kernel estimator by both asymptotic analysis and a simulation study, finding advantages over the fixed kernel approach in both the cases.
Journal ArticleDOI
The data deluge
TL;DR: Archaeology has wandered into exciting but daunting territory. It faces floods of new evidence about the human past that are largely digital, frequently spatial, increasingly open and often remotely sensed as mentioned in this paper.
Journal ArticleDOI
sparr: Analyzing Spatial Relative Risk Using Fixed and Adaptive Kernel Density Estimation in R
TL;DR: The R package sparr is introduced, providing the ability to construct both fixed and adaptive kernel-smoothed densities and risk functions, identify statistically significant fluctuations in an estimated risk function through the use of asymptotic tolerance contours, and visualize these objects in flexible and attractive ways.
References
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Journal ArticleDOI
On Estimation of a Probability Density Function and Mode
TL;DR: In this paper, the problem of the estimation of a probability density function and of determining the mode of the probability function is discussed. Only estimates which are consistent and asymptotically normal are constructed.
Journal ArticleDOI
A spatial scan statistic
TL;DR: In this article, a spatial scan statistic for the detection of clusters in a multi-dimensional point process is proposed, where the area of the scanning window is allowed to vary, and the baseline process may be any inhomogeneous Poisson process or Bernoulli process with intensity pro-portional to some known function.
Journal ArticleDOI
Applied smoothing techniques for data analysis : the kernel approach with S-plus illustrations
Adrian Bowman,Adelchi Azzalini +1 more
TL;DR: 1. Density estimation for exploring data 2. D density estimation for inference 3. Nonparametric regression for explore data 4. Inference with nonparametric regressors 5. Checking parametric regression models 6. Comparing regression curves and surfaces
Journal ArticleDOI
Ratios of Normal Variables and Ratios of Sums of Uniform Variables
TL;DR: In this article, the distribution and density functions of the ratio of two normal random variables were studied in terms of the vivariate normal distribution and the Nicholson's V function, both of which have been extensively studied and for which tables and computational procedures are readily available.
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