Suman Rakshit

Curtin University

StatisticsMetric (mathematics)MathematicsComputer scienceK-function

8Publications

4H-index

52Citations

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Publications 8

Newest

#1Suman Rakshit (Curtin University)H-Index: 4

#2Adrian Baddeley (Curtin University)H-Index: 36

Last. Mark R. Gibberd (Curtin University)H-Index: 16

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Abstract With increasing interest in on-farm experiments, there is a pressing need to develop rigorous statistical methods for analysing these experiments. The adoption of advanced technologies such as yield monitors and variable-rate fertilizer applicators has enabled farmers and researchers to collect biophysical data linked to spatial information at a scale which allows them to investigate the role of spatial variability in the development of optimum management practices. A relevant topic for...

Hierarchical clustering of MS/MS spectra from the firefly metabolome identifies new lucibufagin compounds.

#1Catherine Rawlinson (Curtin University)H-Index: 1

#2Darcy A. B. Jones (Curtin University)H-Index: 2

Last. Paula Moolhuijzen (Curtin University)H-Index: 22

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Metabolite identification is the greatest challenge when analysing metabolomics data, as only a small proportion of metabolite reference standards exist. Clustering MS/MS spectra is a common method to identify similar compounds, however interrogation of underlying signature fragmentation patterns within clusters can be problematic. Previously published high-resolution LC-MS/MS data from the bioluminescent beetle (Photinus pyralis) provided an opportunity to mine new specialized metabolites in th...

#1Adrian Baddeley (Curtin University)H-Index: 36

#2Gopalan Nair (UWA: University of Western Australia)H-Index: 8

Last. Tilman M. Davies (University of Otago)H-Index: 11

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Abstract We review recent research on statistical methods for analysing spatial patterns of points on a network of lines, such as road accident locations along a road network. Due to geometrical complexities, the analysis of such data is extremely challenging, and we describe several common methodological errors. The intrinsic lack of homogeneity in a network militates against the traditional methods of spatial statistics based on stationary processes. Topics include kernel density estimation, r...

#1Suman Rakshit (Curtin University)H-Index: 4

#2Adrian Baddeley (Curtin University)H-Index: 36

Last. Gopalan NairH-Index: 8

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We describe efficient algorithms and open-source code for the second-order statistical analysis of point events on a linear network. Typical summary statistics are adaptations of Ripley's K-function and the pair correlation function to the case of a linear network, with distance measured by the shortest path in the network. Simple implementations consume substantial time and memory. For an efficient implementation, the data structure representing the network must be economical in its use of memo...

#1Suman Rakshit (Curtin University)H-Index: 4

#2Tilman M. Davies (University of Otago)H-Index: 11

Last. Adrian Baddeley (Curtin University)H-Index: 36

view all 7 authors...

#1Suman Rakshit (Curtin University)H-Index: 4

#2Gopalan Nair (UWA: University of Western Australia)H-Index: 8

Last. Adrian Baddeley (Curtin University)H-Index: 36

view all 3 authors...

Abstract The analysis of clustering and correlation between points on a linear network, such as traffic accident locations on a street network, depends crucially on how we measure the distance between points. Standard practice is to measure distance by the length of the shortest path. However, this may be inappropriate and even fallacious in some applications. Alternative distance metrics include Euclidean, least-cost, and resistance distances. This paper develops a general framework for the sec...

#1Adrian Baddeley (Curtin University)H-Index: 36

#2Andrew Hardegen (UWA: University of Western Australia)H-Index: 3

Last. Suman Rakshit (Curtin University)H-Index: 4

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A major weakness of the classical Monte Carlo test is that it is biased when the null hypothesis is composite. This problem persists even when the number of simulations tends to infinity. A standard remedy is to perform a double bootstrap test involving two stages of Monte Carlo simulation: under suitable conditions, this test is asymptotically exact for any fixed significance level. However, the two-stage test is shown to perform poorly in some common applications: for a given number of simulat...

#1Adrian Baddeley (Curtin University)H-Index: 36

#2Gopalan Nair (UWA: University of Western Australia)H-Index: 8

Last. Gregory Edward McSwiggan (UWA: University of Western Australia)H-Index: 2

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