The Experts below are selected from a list of 30933 Experts worldwide ranked by ideXlab platform
Omer Ozturk - One of the best experts on this subject based on the ideXlab platform.
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mixture model analysis of partially rank ordered set samples age groups of fish from length frequency data
Scandinavian Journal of Statistics, 2015Co-Authors: Armin Hatefi, Mohammad Jafari Jozani, Omer OzturkAbstract:type="main" xml:id="sjos12140-abs-0001"> We present a novel methodology for estimating the parameters of a finite mixture model (FMM) based on partially rank-ordered set (PROS) sampling and use it in a fishery application. A PROS sampling design first selects a simple random sample of fish and creates partially rank-ordered judgement subsets by dividing units into subsets of prespecified sizes. The final measurements are then obtained from these partially ordered judgement subsets. The traditional Expectation–Maximization Algorithm is not directly applicable for these observations. We propose a suitable Expectation–Maximization Algorithm to estimate the parameters of the FMMs based on PROS samples. We also study the problem of classification of the PROS sample into the components of the FMM. We show that the maximum likelihood estimators based on PROS samples perform substantially better than their simple random sample counterparts even with small samples. The results are used to classify a fish population using the length-frequency data.
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mixture model analysis of partially rank ordered set samples age groups of fish from length frequency data
Scandinavian Journal of Statistics, 2015Co-Authors: Armin Hatefi, Mohammad Jafari Jozani, Omer OzturkAbstract:We present a novel methodology for estimating the parameters of a finite mixture model (FMM) based on partially rank-ordered set (PROS) sampling and use it in a fishery application. A PROS sampling design first selects a simple random sample of fish and creates partially rank-ordered judgement subsets by dividing units into subsets of prespecified sizes. The final measurements are then obtained from these partially ordered judgement subsets. The traditional Expectation–Maximization Algorithm is not directly applicable for these observations. We propose a suitable Expectation–Maximization Algorithm to estimate the parameters of the FMMs based on PROS samples. We also study the problem of classification of the PROS sample into the components of the FMM. We show that the maximum likelihood estimators based on PROS samples perform substantially better than their simple random sample counterparts even with small samples. The results are used to classify a fish population using the length-frequency data.
Xiangyu Guo - One of the best experts on this subject based on the ideXlab platform.
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robust high dimensional Expectation Maximization Algorithm via trimmed hard thresholding
Machine Learning, 2020Co-Authors: Di Wang, Xiangyu GuoAbstract:In this paper, we study the problem of estimating latent variable models with arbitrarily corrupted samples in high dimensional space (i.e., $$d\gg n$$ ) where the underlying parameter is assumed to be sparse. Specifically, we propose a method called Trimmed (Gradient) Expectation Maximization which adds a trimming gradients step and a hard thresholding step to the Expectation step (E-step) and the Maximization step (M-step), respectively. We show that under some mild assumptions and with an appropriate initialization, the Algorithm is corruption-proofing and converges to the (near) optimal statistical rate geometrically when the fraction of the corrupted samples $$\epsilon$$ is bounded by $${\tilde{O}}\bigg (\frac{1}{\sqrt{n}}\bigg )$$ . Moreover, we apply our general framework to three canonical models: mixture of Gaussians, mixture of regressions and linear regression with missing covariates. Our theory is supported by thorough numerical results.
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robust high dimensional Expectation Maximization Algorithm via trimmed hard thresholding
arXiv: Machine Learning, 2020Co-Authors: Di Wang, Xiangyu GuoAbstract:In this paper, we study the problem of estimating latent variable models with arbitrarily corrupted samples in high dimensional space ({\em i.e.,} $d\gg n$) where the underlying parameter is assumed to be sparse. Specifically, we propose a method called Trimmed (Gradient) Expectation Maximization which adds a trimming gradients step and a hard thresholding step to the Expectation step (E-step) and the Maximization step (M-step), respectively. We show that under some mild assumptions and with an appropriate initialization, the Algorithm is corruption-proofing and converges to the (near) optimal statistical rate geometrically when the fraction of the corrupted samples $\epsilon$ is bounded by $ \tilde{O}(\frac{1}{\sqrt{n}})$. Moreover, we apply our general framework to three canonical models: mixture of Gaussians, mixture of regressions and linear regression with missing covariates. Our theory is supported by thorough numerical results.
Pedro Vilanova - One of the best experts on this subject based on the ideXlab platform.
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An Efficient Forward-Reverse Expectation-Maximization Algorithm for Statistical Inference in Stochastic Reaction Networks
Stochastic Analysis and Applications, 2016Co-Authors: Christian Bayer, Alvaro Moraes, Raul Tempone, Pedro VilanovaAbstract:ABSTRACTIn this work, we present an extension of the forward–reverse representation introduced by Bayer and Schoenmakers (Annals of Applied Probability, 24(5):1994–2032, 2014) to the context of stochastic reaction networks (SRNs). We apply this stochastic representation to the computation of efficient approximations of expected values of functionals of SRN bridges, that is, SRNs conditional on their values in the extremes of given time intervals. We then employ this SRN bridge-generation technique to the statistical inference problem of approximating reaction propensities based on discretely observed data. To this end, we introduce a two-phase iterative inference method in which, during phase I, we solve a set of deterministic optimization problems where the SRNs are replaced by their reaction-rate ordinary differential equations approximation; then, during phase II, we apply the Monte Carlo version of the Expectation-Maximization Algorithm to the phase I output. By selecting a set of overdispersed seeds ...
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an efficient forward reverse Expectation Maximization Algorithm for statistical inference in stochastic reaction networks
arXiv: Numerical Analysis, 2015Co-Authors: Christian Bayer, Alvaro Moraes, Raul Tempone, Pedro VilanovaAbstract:In this work, we present an extension to the context of Stochastic Reaction Networks (SRNs) of the forward-reverse representation introduced in "Simulation of forward-reverse stochastic representations for conditional diffusions", a 2014 paper by Bayer and Schoenmakers. We apply this stochastic representation in the computation of efficient approximations of expected values of functionals of SNR bridges, i.e., SRNs conditioned to its values in the extremes of given time-intervals. We then employ this SNR bridge-generation technique to the statistical inference problem of approximating the reaction propensities based on discretely observed data. To this end, we introduce a two-phase iterative inference method in which, during phase I, we solve a set of deterministic optimization problems where the SRNs are replaced by their reaction-rate Ordinary Differential Equations (ODEs) approximation; then, during phase II, we apply the Monte Carlo version of the Expectation-Maximization (EM) Algorithm starting from the phase I output. By selecting a set of over dispersed seeds as initial points for phase I, the output of parallel runs from our two-phase method is a cluster of approximate maximum likelihood estimates. Our results are illustrated by numerical examples.
Roberto Horowitz - One of the best experts on this subject based on the ideXlab platform.
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Prediction on Travel-Time Distribution for Freeways Using Online Expectation Maximization Algorithm
TRB, 2014Co-Authors: Nianfeng Wan, Gabriel Gomes, Roberto HorowitzAbstract:This paper presents a stochastic model-based approach to freeway travel-time prediction. The approach uses the Link-Node Cell Transmission Model (LN-CTM) to model traffic and provides a probability distribution for travel time. On-ramp and mainline flow profiles are collected from loop detectors, along with their uncertainties. The probability distribution is generated using Monte Carlo simulation and the Online Expectation Maximization clustering Algorithm. The simulation is implemented with a reasonable stopping criterion in order to reduce sample size requirement. Results show that the approach is able to generate an accurate multimodal distribution for travel-time. Future improvements are also discussed.
Armin Hatefi - One of the best experts on this subject based on the ideXlab platform.
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mixture model analysis of partially rank ordered set samples age groups of fish from length frequency data
Scandinavian Journal of Statistics, 2015Co-Authors: Armin Hatefi, Mohammad Jafari Jozani, Omer OzturkAbstract:type="main" xml:id="sjos12140-abs-0001"> We present a novel methodology for estimating the parameters of a finite mixture model (FMM) based on partially rank-ordered set (PROS) sampling and use it in a fishery application. A PROS sampling design first selects a simple random sample of fish and creates partially rank-ordered judgement subsets by dividing units into subsets of prespecified sizes. The final measurements are then obtained from these partially ordered judgement subsets. The traditional Expectation–Maximization Algorithm is not directly applicable for these observations. We propose a suitable Expectation–Maximization Algorithm to estimate the parameters of the FMMs based on PROS samples. We also study the problem of classification of the PROS sample into the components of the FMM. We show that the maximum likelihood estimators based on PROS samples perform substantially better than their simple random sample counterparts even with small samples. The results are used to classify a fish population using the length-frequency data.
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mixture model analysis of partially rank ordered set samples age groups of fish from length frequency data
Scandinavian Journal of Statistics, 2015Co-Authors: Armin Hatefi, Mohammad Jafari Jozani, Omer OzturkAbstract:We present a novel methodology for estimating the parameters of a finite mixture model (FMM) based on partially rank-ordered set (PROS) sampling and use it in a fishery application. A PROS sampling design first selects a simple random sample of fish and creates partially rank-ordered judgement subsets by dividing units into subsets of prespecified sizes. The final measurements are then obtained from these partially ordered judgement subsets. The traditional Expectation–Maximization Algorithm is not directly applicable for these observations. We propose a suitable Expectation–Maximization Algorithm to estimate the parameters of the FMMs based on PROS samples. We also study the problem of classification of the PROS sample into the components of the FMM. We show that the maximum likelihood estimators based on PROS samples perform substantially better than their simple random sample counterparts even with small samples. The results are used to classify a fish population using the length-frequency data.
