{"title":"空间采样和滤波","authors":"A. Baddeley","doi":"10.1201/9780203738276-2","DOIUrl":null,"url":null,"abstract":"When a spatial pattern is observed through a bounded window, inference about the pattern is hampered by sampling eeects known as \\edge eeects\". This chapter identiies two main types of edge eeects: size-dependent sampling bias and censoring eeects. Sampling bias can be eliminated by changing the sampling technique, or`corrected' by weighting the observations. Censoring eeects can be tackled using the methods of survival analysis.","PeriodicalId":437493,"journal":{"name":"Stochastic Geometry","volume":"93 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"29","resultStr":"{\"title\":\"Spatial sampling and censoring\",\"authors\":\"A. Baddeley\",\"doi\":\"10.1201/9780203738276-2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"When a spatial pattern is observed through a bounded window, inference about the pattern is hampered by sampling eeects known as \\\\edge eeects\\\". This chapter identiies two main types of edge eeects: size-dependent sampling bias and censoring eeects. Sampling bias can be eliminated by changing the sampling technique, or`corrected' by weighting the observations. Censoring eeects can be tackled using the methods of survival analysis.\",\"PeriodicalId\":437493,\"journal\":{\"name\":\"Stochastic Geometry\",\"volume\":\"93 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"29\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Stochastic Geometry\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1201/9780203738276-2\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Stochastic Geometry","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1201/9780203738276-2","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
When a spatial pattern is observed through a bounded window, inference about the pattern is hampered by sampling eeects known as \edge eeects". This chapter identiies two main types of edge eeects: size-dependent sampling bias and censoring eeects. Sampling bias can be eliminated by changing the sampling technique, or`corrected' by weighting the observations. Censoring eeects can be tackled using the methods of survival analysis.
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