{"title":"Introduction to Random Fields and Scale Invariance","authors":"H. Biermé","doi":"10.1007/978-3-030-13547-8_4","DOIUrl":"https://doi.org/10.1007/978-3-030-13547-8_4","url":null,"abstract":"","PeriodicalId":437493,"journal":{"name":"Stochastic Geometry","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133458936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Spatial sampling and censoring","authors":"A. Baddeley","doi":"10.1201/9780203738276-2","DOIUrl":"https://doi.org/10.1201/9780203738276-2","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":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123058267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
求助内容：
应助结果提醒方式：