Empirical Euclidean likelihood for general estimating equations for association dependent processes is investigated. The strong consistency and asymptotic normality of the blockwise maximum empirical Euclidean likelih...Empirical Euclidean likelihood for general estimating equations for association dependent processes is investigated. The strong consistency and asymptotic normality of the blockwise maximum empirical Euclidean likelihood estimator are presented. We show that it is more efficient than estimator without blocking. The blockwise empirical Euclidean log-likelihood ratio asymptotically follows a chi-square distribution.展开更多
Background:Ecological processes such as seedling establishment,biotic interactions,and mortality can leave footprints on species spatial structure that can be detectable through spatial point-pattern analysis(SPPA).Be...Background:Ecological processes such as seedling establishment,biotic interactions,and mortality can leave footprints on species spatial structure that can be detectable through spatial point-pattern analysis(SPPA).Being widely used in plant ecology,SPPA is increasingly carried out to describe biotic interactions and interpret patternprocess relationships.However,some aspects are still subjected to a non-negligible debate such as required sample size(in terms of the number of points and plot area),the link between the low number of points and frequently observed random(or independent)patterns,and relating patterns to processes.In this paper,an overview of SPPA is given based on rich and updated literature providing guidance for ecologists(especially beginners)on summary statistics,uni-/bi-/multivariate analysis,unmarked/marked analysis,types of marks,etc.Some ambiguities in SPPA are also discussed.Results:SPPA has a long history in plant ecology and is based on a large set of summary statistics aiming to describe species spatial patterns.Several mechanisms known to be responsible for species spatial patterns are actually investigated in different biomes and for different species.Natural processes,plant environmental conditions,and human intervention are interrelated and are key drivers of plant spatial distribution.In spite of being not recommended,small sample sizes are more common in SPPA.In some areas,periodic forest inventories and permanent plots are scarce although they are key tools for spatial data availability and plant dynamic monitoring.Conclusion:The spatial position of plants is an interesting source of information that helps to make hypotheses about processes responsible for plant spatial structures.Despite the continuous progress of SPPA,some ambiguities require further clarifications.展开更多
Empirical likelihood is discussed by using the blockwise technique for strongly stationary, positively associated random variables. Our results show that the statistics is asymptotically chi-square distributed and the...Empirical likelihood is discussed by using the blockwise technique for strongly stationary, positively associated random variables. Our results show that the statistics is asymptotically chi-square distributed and the corresponding confidence interval can be constructed.展开更多
Apriori algorithm is often used in traditional association rules mining,searching for the mode of higher frequency.Then the correlation rules are obtained by detected the correlation of the item sets,but this tends to...Apriori algorithm is often used in traditional association rules mining,searching for the mode of higher frequency.Then the correlation rules are obtained by detected the correlation of the item sets,but this tends to ignore low-support high-correlation of association rules.In view of the above problems,some scholars put forward the positive correlation coefficient based on Phi correlation to avoid the embarrassment caused by Apriori algorithm.It can dig item sets with low-support but high-correlation.Although the algorithm has pruned the search space,it is not obvious that the performance of the running time based on the big data set is reduced,and the correlation pairs can be meaningless.This paper presents an improved mining algorithm with new association rules based on interestingness for correlation pairs,using an upper bound on interestingness of the supersets to prune the search space.It greatly reduces the running time,and filters the meaningless correlation pairs according to the constraints of the redundancy.Compared with the algorithm based on the Phi correlation coefficient,the new algorithm has been significantly improved in reducing the running time,the result has pruned the redundant correlation pairs.So it improves the mining efficiency and accuracy.展开更多
基金Supported by the National Natural Science Foundation of China (10771192)the Zhejiang Natural Science Foundation (J20091364)
文摘Empirical Euclidean likelihood for general estimating equations for association dependent processes is investigated. The strong consistency and asymptotic normality of the blockwise maximum empirical Euclidean likelihood estimator are presented. We show that it is more efficient than estimator without blocking. The blockwise empirical Euclidean log-likelihood ratio asymptotically follows a chi-square distribution.
文摘Background:Ecological processes such as seedling establishment,biotic interactions,and mortality can leave footprints on species spatial structure that can be detectable through spatial point-pattern analysis(SPPA).Being widely used in plant ecology,SPPA is increasingly carried out to describe biotic interactions and interpret patternprocess relationships.However,some aspects are still subjected to a non-negligible debate such as required sample size(in terms of the number of points and plot area),the link between the low number of points and frequently observed random(or independent)patterns,and relating patterns to processes.In this paper,an overview of SPPA is given based on rich and updated literature providing guidance for ecologists(especially beginners)on summary statistics,uni-/bi-/multivariate analysis,unmarked/marked analysis,types of marks,etc.Some ambiguities in SPPA are also discussed.Results:SPPA has a long history in plant ecology and is based on a large set of summary statistics aiming to describe species spatial patterns.Several mechanisms known to be responsible for species spatial patterns are actually investigated in different biomes and for different species.Natural processes,plant environmental conditions,and human intervention are interrelated and are key drivers of plant spatial distribution.In spite of being not recommended,small sample sizes are more common in SPPA.In some areas,periodic forest inventories and permanent plots are scarce although they are key tools for spatial data availability and plant dynamic monitoring.Conclusion:The spatial position of plants is an interesting source of information that helps to make hypotheses about processes responsible for plant spatial structures.Despite the continuous progress of SPPA,some ambiguities require further clarifications.
基金the National Natural Science Foundation of China(No.10661003)
文摘Empirical likelihood is discussed by using the blockwise technique for strongly stationary, positively associated random variables. Our results show that the statistics is asymptotically chi-square distributed and the corresponding confidence interval can be constructed.
基金This research was supported by the National Natural Science Foundation of China under Grant No.61772280by the China Special Fund for Meteorological Research in the Public Interest under Grant GYHY201306070by the Jiangsu Province Innovation and Entrepreneurship Training Program for College Students under Grant No.201810300079X.
文摘Apriori algorithm is often used in traditional association rules mining,searching for the mode of higher frequency.Then the correlation rules are obtained by detected the correlation of the item sets,but this tends to ignore low-support high-correlation of association rules.In view of the above problems,some scholars put forward the positive correlation coefficient based on Phi correlation to avoid the embarrassment caused by Apriori algorithm.It can dig item sets with low-support but high-correlation.Although the algorithm has pruned the search space,it is not obvious that the performance of the running time based on the big data set is reduced,and the correlation pairs can be meaningless.This paper presents an improved mining algorithm with new association rules based on interestingness for correlation pairs,using an upper bound on interestingness of the supersets to prune the search space.It greatly reduces the running time,and filters the meaningless correlation pairs according to the constraints of the redundancy.Compared with the algorithm based on the Phi correlation coefficient,the new algorithm has been significantly improved in reducing the running time,the result has pruned the redundant correlation pairs.So it improves the mining efficiency and accuracy.
