The Experts below are selected from a list of 108 Experts worldwide ranked by ideXlab platform
Kenichi Ozaki - One of the best experts on this subject based on the ideXlab platform.
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Cost-effective sampling for estimating species richness of ground beetles (Coleoptera: Carabidae) using pitfall traps: efficiency of non-parametric species richness Estimators
Applied Entomology and Zoology, 2019Co-Authors: Satoshi Yamanaka, Yutaka Hironaka, Kenichi OzakiAbstract:Because it is extremely time-consuming and costly to evaluate species richness of highly diverse taxa such as insects, cost-effective sampling strategies to estimate species richness are needed, especially when long-term monitoring is conducted. We found a sampling method that reduced the number of traps per site when estimating species richness of ground beetles (Coleoptera: Carabidae) in six forest sites in Sorachi, Hokkaido, northern Japan. We set 40 pitfall traps per site and collected ground beetles for 1-week periods in June, July, and August 2013. We evaluated the performance of three non-parametric species richness Estimators [Jackknife2, Chao1, and the abundance-based coverage Estimator (ACE)] to estimate species richness. Rarefaction curves showed that ACE was the only Valid Estimator to estimate species richness for all sites. Using ACE, we can reduce the number of traps to 30 or 20 from 40 traps per site if we allow a bias of 5 or 10%, respectively. This study suggests that non-parametric species richness Estimators are useful in estimating species richness of ground beetles with reduced sample size.
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Cost-effective sampling for estimating species richness of ground beetles (Coleoptera: Carabidae) using pitfall traps: efficiency of non-parametric species richness Estimators
Applied Entomology and Zoology, 2019Co-Authors: Satoshi Yamanaka, Yutaka Hironaka, Kenichi OzakiAbstract:Because it is extremely time-consuming and costly to evaluate species richness of highly diverse taxa such as insects, cost-effective sampling strategies to estimate species richness are needed, especially when long-term monitoring is conducted. We found a sampling method that reduced the number of traps per site when estimating species richness of ground beetles (Coleoptera: Carabidae) in six forest sites in Sorachi, Hokkaido, northern Japan. We set 40 pitfall traps per site and collected ground beetles for 1-week periods in June, July, and August 2013. We evaluated the performance of three non-parametric species richness Estimators [Jackknife2, Chao1, and the abundance-based coverage Estimator (ACE)] to estimate species richness. Rarefaction curves showed that ACE was the only Valid Estimator to estimate species richness for all sites. Using ACE, we can reduce the number of traps to 30 or 20 from 40 traps per site if we allow a bias of 5 or 10%, respectively. This study suggests that non-parametric species richness Estimators are useful in estimating species richness of ground beetles with reduced sample size.
Kani Chen - One of the best experts on this subject based on the ideXlab platform.
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A General Pairwise Comparison Model for Extremely Sparse Networks
arXiv: Machine Learning, 2020Co-Authors: Ruijian Han, Kani ChenAbstract:Statistical inference using pairwise comparison data has been an effective approach to analyzing complex and sparse networks. In this paper we propose a general framework for modeling the mutual interaction in a probabilistic network, which enjoys ample flexibility in terms of parametrization. Within this set-up, we establish that the maximum likelihood Estimator (MLE) for the latent scores of the subjects is uniformly consistent under a near-minimal condition on network sparsity. This condition is sharp in terms of the leading order asymptotics describing the sparsity. The proof utilizes a novel chaining technique based on the error-induced metric as well as careful counting of comparison graph structures. Our results guarantee that the MLE is a Valid Estimator for inference in large-scale comparison networks where data is asymptotically deficient. Numerical simulations are provided to complement the theoretical analysis.
Haris Vikalo - One of the best experts on this subject based on the ideXlab platform.
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online topology inference from streaming stationary graph signals
2019 IEEE Data Science Workshop (DSW), 2019Co-Authors: Rasoul Shafipour, Abolfazl Hashemi, Gonzalo Mateos, Haris VikaloAbstract:We address the problem of online topology inference from streaming nodal observations of graph signals generated by linear diffusion dynamics on the sought graph. To that end, we leverage the stationarity of the signals and use the so-called graph-shift operator (GSO) as a matrix representation of the graph. Under this model, estimated covariance eigenvectors obtained from streaming independent graph signals diffused on the sought network are a Valid Estimator of the GSO’s spectral templates. We develop an ADMM algorithm to find a sparse and structurally admissible GSO given the eigenvectors estimate. Then, we propose an online scheme that upon sensing new diffused observations, efficiently updates eigenvectors (thus makes more accurate on expectation) and performs only one or a few iterations of the mentioned ADMM until the new data is observed. Numerical tests illustrate the effectiveness of the proposed topology inference approach in recovering large scale graphs, adapting to streaming information, and accommodating changes in the sought network.
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DSW - Online Topology Inference from Streaming Stationary Graph Signals
2019 IEEE Data Science Workshop (DSW), 2019Co-Authors: Rasoul Shafipour, Abolfazl Hashemi, Gonzalo Mateos, Haris VikaloAbstract:We address the problem of online topology inference from streaming nodal observations of graph signals generated by linear diffusion dynamics on the sought graph. To that end, we leverage the stationarity of the signals and use the so-called graph-shift operator (GSO) as a matrix representation of the graph. Under this model, estimated covariance eigenvectors obtained from streaming independent graph signals diffused on the sought network are a Valid Estimator of the GSO’s spectral templates. We develop an ADMM algorithm to find a sparse and structurally admissible GSO given the eigenvectors estimate. Then, we propose an online scheme that upon sensing new diffused observations, efficiently updates eigenvectors (thus makes more accurate on expectation) and performs only one or a few iterations of the mentioned ADMM until the new data is observed. Numerical tests illustrate the effectiveness of the proposed topology inference approach in recovering large scale graphs, adapting to streaming information, and accommodating changes in the sought network.
Satoshi Yamanaka - One of the best experts on this subject based on the ideXlab platform.
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Cost-effective sampling for estimating species richness of ground beetles (Coleoptera: Carabidae) using pitfall traps: efficiency of non-parametric species richness Estimators
Applied Entomology and Zoology, 2019Co-Authors: Satoshi Yamanaka, Yutaka Hironaka, Kenichi OzakiAbstract:Because it is extremely time-consuming and costly to evaluate species richness of highly diverse taxa such as insects, cost-effective sampling strategies to estimate species richness are needed, especially when long-term monitoring is conducted. We found a sampling method that reduced the number of traps per site when estimating species richness of ground beetles (Coleoptera: Carabidae) in six forest sites in Sorachi, Hokkaido, northern Japan. We set 40 pitfall traps per site and collected ground beetles for 1-week periods in June, July, and August 2013. We evaluated the performance of three non-parametric species richness Estimators [Jackknife2, Chao1, and the abundance-based coverage Estimator (ACE)] to estimate species richness. Rarefaction curves showed that ACE was the only Valid Estimator to estimate species richness for all sites. Using ACE, we can reduce the number of traps to 30 or 20 from 40 traps per site if we allow a bias of 5 or 10%, respectively. This study suggests that non-parametric species richness Estimators are useful in estimating species richness of ground beetles with reduced sample size.
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Cost-effective sampling for estimating species richness of ground beetles (Coleoptera: Carabidae) using pitfall traps: efficiency of non-parametric species richness Estimators
Applied Entomology and Zoology, 2019Co-Authors: Satoshi Yamanaka, Yutaka Hironaka, Kenichi OzakiAbstract:Because it is extremely time-consuming and costly to evaluate species richness of highly diverse taxa such as insects, cost-effective sampling strategies to estimate species richness are needed, especially when long-term monitoring is conducted. We found a sampling method that reduced the number of traps per site when estimating species richness of ground beetles (Coleoptera: Carabidae) in six forest sites in Sorachi, Hokkaido, northern Japan. We set 40 pitfall traps per site and collected ground beetles for 1-week periods in June, July, and August 2013. We evaluated the performance of three non-parametric species richness Estimators [Jackknife2, Chao1, and the abundance-based coverage Estimator (ACE)] to estimate species richness. Rarefaction curves showed that ACE was the only Valid Estimator to estimate species richness for all sites. Using ACE, we can reduce the number of traps to 30 or 20 from 40 traps per site if we allow a bias of 5 or 10%, respectively. This study suggests that non-parametric species richness Estimators are useful in estimating species richness of ground beetles with reduced sample size.
Ruijian Han - One of the best experts on this subject based on the ideXlab platform.
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A General Pairwise Comparison Model for Extremely Sparse Networks
arXiv: Machine Learning, 2020Co-Authors: Ruijian Han, Kani ChenAbstract:Statistical inference using pairwise comparison data has been an effective approach to analyzing complex and sparse networks. In this paper we propose a general framework for modeling the mutual interaction in a probabilistic network, which enjoys ample flexibility in terms of parametrization. Within this set-up, we establish that the maximum likelihood Estimator (MLE) for the latent scores of the subjects is uniformly consistent under a near-minimal condition on network sparsity. This condition is sharp in terms of the leading order asymptotics describing the sparsity. The proof utilizes a novel chaining technique based on the error-induced metric as well as careful counting of comparison graph structures. Our results guarantee that the MLE is a Valid Estimator for inference in large-scale comparison networks where data is asymptotically deficient. Numerical simulations are provided to complement the theoretical analysis.