| タイトル | Bayesian Hierarchical Models for Spatially Misaligned Data in R |
| 本文（外部サイト） | http://hdl.handle.net/2060/20150021198 |
| 著者（英） | Banerjee, Sudipto; Cook, Bruce D.; Finley, Andrew O. |
| 著者所属（英） | NASA Goddard Space Flight Center |
| 発行日 | 2014-06-15 |
| 言語 | eng |
| 内容記述 | Investment in long-term monitoring networks and advancement in sensor technologies are creating data-rich environments that provide extraordinary opportunities to understand the complexity of large and spatially indexed ecological data. Building such understanding often requires the analysis of spatially indexed data sets with multiple variables measured at each location. In such settings, it is commonly posited that there is association between the measurements at a given location as well as association among measurements across locations. In ecological analysis, we often seek inference about the association among these multiple variables or wish to predict their values at new locations. For example, consider the analysis of (i) species co-occurrence where species' presence/ absence or abundance is recorded at each location, for example Ovaskainen, Hottola & Siitonen (2010); (ii) soil nutrient impact on local tree growth and competition where soil nutrient measurements coincide with tree inventory locations, for example Baribault, Kobe & Finley (2012); or (iii) relationship between multiple environmental stressors and measures of focal species fitness, for example Swope & Parker (2012). In each case, development of a statistical model typically requires the full set of outcomes, for example species presence/ absence, and covariates, for example soil nutrients or environmental stressors, at a set of locations. |
| NASA分類 | Earth Resources and Remote Sensing; Statistics and Probability |
| レポートNO | GSFC-E-DAA-TN22354 |
| 権利 | Copyright, Distribution as joint owner in the copyright |
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