{"title":"具有位置依赖权的随机几何图的最小生成树","authors":"Ghurumuruhan Ganesan","doi":"10.3150/20-BEJ1318","DOIUrl":null,"url":null,"abstract":"Consider n nodes {Xi}1≤i≤n independently distributed in the unit square S, each according to a distribution f. Nodes Xi and Xj are joined by an edge if the Euclidean distance d(Xi,Xj) is less than rn, the adjacency distance and the resulting random graph Gn is called a random geometric graph (RGG). We now assign a location dependent weight to each edge of Gn and define MSTn to be the sum of the weights of the minimum spanning trees of all components of Gn. For values of rn above the connectivity regime, we obtain upper and lower bound deviation estimates for MSTn and L2-convergence of MSTn appropriately scaled and centred.","PeriodicalId":55387,"journal":{"name":"Bernoulli","volume":"27 1","pages":"2473-2493"},"PeriodicalIF":1.5000,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Minimum spanning trees of random geometric graphs with location dependent weights\",\"authors\":\"Ghurumuruhan Ganesan\",\"doi\":\"10.3150/20-BEJ1318\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Consider n nodes {Xi}1≤i≤n independently distributed in the unit square S, each according to a distribution f. Nodes Xi and Xj are joined by an edge if the Euclidean distance d(Xi,Xj) is less than rn, the adjacency distance and the resulting random graph Gn is called a random geometric graph (RGG). We now assign a location dependent weight to each edge of Gn and define MSTn to be the sum of the weights of the minimum spanning trees of all components of Gn. For values of rn above the connectivity regime, we obtain upper and lower bound deviation estimates for MSTn and L2-convergence of MSTn appropriately scaled and centred.\",\"PeriodicalId\":55387,\"journal\":{\"name\":\"Bernoulli\",\"volume\":\"27 1\",\"pages\":\"2473-2493\"},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2021-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Bernoulli\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://doi.org/10.3150/20-BEJ1318\",\"RegionNum\":2,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"STATISTICS & PROBABILITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bernoulli","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.3150/20-BEJ1318","RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
Minimum spanning trees of random geometric graphs with location dependent weights
Consider n nodes {Xi}1≤i≤n independently distributed in the unit square S, each according to a distribution f. Nodes Xi and Xj are joined by an edge if the Euclidean distance d(Xi,Xj) is less than rn, the adjacency distance and the resulting random graph Gn is called a random geometric graph (RGG). We now assign a location dependent weight to each edge of Gn and define MSTn to be the sum of the weights of the minimum spanning trees of all components of Gn. For values of rn above the connectivity regime, we obtain upper and lower bound deviation estimates for MSTn and L2-convergence of MSTn appropriately scaled and centred.
期刊介绍:
BERNOULLI is the journal of the Bernoulli Society for Mathematical Statistics and Probability, issued four times per year. The journal provides a comprehensive account of important developments in the fields of statistics and probability, offering an international forum for both theoretical and applied work.
BERNOULLI will publish:
Papers containing original and significant research contributions: with background, mathematical derivation and discussion of the results in suitable detail and, where appropriate, with discussion of interesting applications in relation to the methodology proposed.
Papers of the following two types will also be considered for publication, provided they are judged to enhance the dissemination of research:
Review papers which provide an integrated critical survey of some area of probability and statistics and discuss important recent developments.
Scholarly written papers on some historical significant aspect of statistics and probability.