The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Nitis Mukhopadhyay - One of the best experts on this subject based on the ideXlab platform.
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On general asymptotically second-order efficient purely sequential fixed-width confidence interval (FWCI) and minimum risk Point Estimation (MRPE) strategies for a normal mean and optimality
METRON, 2020Co-Authors: Nitis Mukhopadhyay, Srawan Kumar BishnoiAbstract:We develop a generalized class of purely sequential sampling strategies associated with both fixed-width confidence interval (FWCI) and minimum risk Point Estimation (MRPE) problems for the unknown mean $$\mu $$ μ of a normally distributed population having its variance $$\sigma ^{2}$$ σ 2 also unknown. Under this newly proposed general class of associated Estimation strategies, we develop a variety of asymptotic first-order and asymptotic second-order properties such as asymptotic consistency, first-order efficiency, first-order risk efficiency, second-order efficiency, and second-order regret analysis. Next, we proceed to locate an optimal strategy within our newly built large class of possibilities. Such optimality is defined as having been associated with the minimal second-order asymptotic variance of a stopping time within the general class of proposed strategies. We follow through by exploring both the FWCI and MRPE problems with the help of data analysis from simulations.
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minimum risk Point Estimation mrpe of the mean in an exponential distribution under powered absolute error loss pael due to Estimation plus cost of sampling
Sequential Analysis, 2020Co-Authors: Nitis Mukhopadhyay, Yakov KharitonAbstract:We begin with a review of asymptotic properties of a purely sequential minimum risk Point Estimation (MRPE) methodology for an unknown mean in a one-parameter exponential distribution under a class...
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a general theory of purely sequential minimum risk Point Estimation mrpe of a function of the mean in a normal distribution
Sequential Analysis, 2019Co-Authors: Nitis Mukhopadhyay, Zhe WangAbstract:A purely sequential minimum risk Point Estimation (MRPE) methodology with associated stopping time N is designed to come up with a useful Estimation strategy. We work under an appropriately formula...
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discussion on sequential Estimation for time series models by t n sriram and ross iaci
Sequential Analysis, 2014Co-Authors: Nitis Mukhopadhyay, Swarnali BanerjeeAbstract:Abstract Among other issues, Sriram and Iaci (2014) have elegantly drawn attention to the notion of “negative regret” for a number of interesting sequential minimum risk Point Estimation problems under a time series model. We briefly lay down our thoughts and understanding with regard to possible negative regret in some of the Point Estimation problems.
Luis Salgado - One of the best experts on this subject based on the ideXlab platform.
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real time vanishing Point Estimation in road sequences using adaptive steerable filter banks
Advanced Concepts for Intelligent Vision Systems, 2007Co-Authors: Marcos Nieto, Luis SalgadoAbstract:This paper presents an innovative road modeling strategy for video-based driver assistance systems. It is based on the real-time Estimation of the vanishing Point of sequences captured with forward looking cameras located near the rear view mirror of a vehicle. The vanishing Point is used for many purposes in video-based driver assistance systems, such as computing linear models of the road, extraction of calibration parameters of the camera, stabilization of sequences, etc. In this work, a novel strategy for vanishing Point Estimation is presented. It is based on the use of an adaptive steerable filter bank which enhances lane markings according to their expected orientations. Very accurate results are obtained in the computation of the vanishing Point for several type of sequences, including overtaking traffic, changing illumination conditions, paintings in the road, etc.
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stabilization of inverse perspective mapping images based on robust vanishing Point Estimation
IEEE Intelligent Vehicles Symposium, 2007Co-Authors: Marcos Nieto, Luis Salgado, Fernando Jaureguizar, Julian CabreraAbstract:In this work, a new inverse perspective mapping (IPM) technique is proposed based on a robust Estimation of the vanishing Point, which provide bird-view images of the road, so that facilitating the tasks of road modeling and vehicle detection and tracking. This new approach has been design to cope with the instability that cameras mounted on a moving vehicle suffer. The Estimation of the vanishing Point relies on a novel and efficient feature extraction strategy, which segmentates the lane markings of the images by combining a histogram-based segmentation with temporal and frequency filtering. Then, the vanishing Point of each image is stabilized by means of a temporal filtering along the estimates of previous images. In a last step, the IPM image is computed based on the stabilized vanishing Point. Tests have been carried out on several long video sequences captured from cameras inside a vehicle being driven along highways and local roads, with different illumination and weather conditions, presence of shadows, occluding vehicles, and slope changes. Results have shown a significant improvement in terms of lane width constancy and parallelism between lane markings over non-stabilized IPM algorithms.
Marcos Nieto - One of the best experts on this subject based on the ideXlab platform.
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real time vanishing Point Estimation in road sequences using adaptive steerable filter banks
Advanced Concepts for Intelligent Vision Systems, 2007Co-Authors: Marcos Nieto, Luis SalgadoAbstract:This paper presents an innovative road modeling strategy for video-based driver assistance systems. It is based on the real-time Estimation of the vanishing Point of sequences captured with forward looking cameras located near the rear view mirror of a vehicle. The vanishing Point is used for many purposes in video-based driver assistance systems, such as computing linear models of the road, extraction of calibration parameters of the camera, stabilization of sequences, etc. In this work, a novel strategy for vanishing Point Estimation is presented. It is based on the use of an adaptive steerable filter bank which enhances lane markings according to their expected orientations. Very accurate results are obtained in the computation of the vanishing Point for several type of sequences, including overtaking traffic, changing illumination conditions, paintings in the road, etc.
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stabilization of inverse perspective mapping images based on robust vanishing Point Estimation
IEEE Intelligent Vehicles Symposium, 2007Co-Authors: Marcos Nieto, Luis Salgado, Fernando Jaureguizar, Julian CabreraAbstract:In this work, a new inverse perspective mapping (IPM) technique is proposed based on a robust Estimation of the vanishing Point, which provide bird-view images of the road, so that facilitating the tasks of road modeling and vehicle detection and tracking. This new approach has been design to cope with the instability that cameras mounted on a moving vehicle suffer. The Estimation of the vanishing Point relies on a novel and efficient feature extraction strategy, which segmentates the lane markings of the images by combining a histogram-based segmentation with temporal and frequency filtering. Then, the vanishing Point of each image is stabilized by means of a temporal filtering along the estimates of previous images. In a last step, the IPM image is computed based on the stabilized vanishing Point. Tests have been carried out on several long video sequences captured from cameras inside a vehicle being driven along highways and local roads, with different illumination and weather conditions, presence of shadows, occluding vehicles, and slope changes. Results have shown a significant improvement in terms of lane width constancy and parallelism between lane markings over non-stabilized IPM algorithms.
Brooke K Mayer - One of the best experts on this subject based on the ideXlab platform.
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improved virus isoelectric Point Estimation by exclusion of known and predicted genome binding regions
Applied and Environmental Microbiology, 2020Co-Authors: Joe Heffron, Brooke K MayerAbstract:ABSTRACT Knowledge of the isoelectric Points (pIs) of viruses is beneficial for predicting virus behavior in environmental transport and physical/chemical treatment applications. However, the empirically measured pIs of many viruses have thus far defied simple explanation, let alone prediction, based on the ionizable amino acid composition of the virus capsid. Here, we suggest an approach for predicting the pI of nonenveloped viruses by excluding capsid regions that stabilize the virus polynucleotide via electrostatic interactions. This method was applied first to viruses with known polynucleotide-binding regions (PBRs) and/or three-dimensional (3D) structures. Then, PBRs were predicted in a group of 32 unique viral capsid proteome sequences via conserved structures and sequence motifs. Removing predicted PBRs resulted in a significantly better fit to empirical pI values. After modification, mean differences between theoretical and empirical pI values were reduced from 2.1 ± 2.4 to 0.1 ± 1.7 pH units. IMPORTANCE This model fits predicted pIs to empirical values for a diverse set of viruses. The results suggest that many previously reported discrepancies between theoretical and empirical virus pIs can be explained by coulombic neutralization of PBRs of the inner capsid. Given the diversity of virus capsid structures, this nonarbitrary, heuristic approach to predicting virus pI offers an effective alternative to a simplistic, one-size-fits-all charge model of the virion. The accurate, structure-based prediction of PBRs of the virus capsid employed here may also be of general interest to structural virologists.
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improved virus isoelectric Point Estimation by exclusion of known and predicted genome binding regions
bioRxiv, 2020Co-Authors: Joe Heffron, Brooke K MayerAbstract:Abstract Accurate prediction of the isoelectric Point (pI) of viruses is beneficial for modeling virus behavior in environmental transport and physical/chemical treatment applications. However, the empirically measured pIs of many viruses have thus far defied simple explanation, let alone prediction, based on the ionizable amino acid composition of the virus capsid. Here, we suggest an approach for predicting virus pI by excluding capsid regions that stabilize the virus polynucleotide via electrostatic interactions. This method was applied first to viruses with known polynucleotide-binding regions (PBRs) and/or 3D structures. Then, PBRs were predicted in a group of 32 unique viral capsid proteome sequences via conserved structures and sequence motifs. Removing predicted PBRs resulted in a significantly better fit to empirical pI values. After modification, mean differences between theoretical and empirical pI values were reduced from 2.1 ± 2.4 to 0.1 ± 1.7 pH units. Importance This model is the first to fit predicted pIs to empirical values for a diverse set of viruses. The results suggest that many previously-reported discrepancies between theoretical and empirical virus pIs can be explained by coulombic neutralization of PBRs of the inner capsid. Given the diversity of virus capsid structures, this nonarbitrary, heuristic approach to predicting virus pI offers an effective alternative to a simplistic, one-size-fits-all charge model of the virion. The accurate, structure-based prediction of PBRs of the virus capsid employed here may also be of general interest to structural virologists.
Pablo Barbera - One of the best experts on this subject based on the ideXlab platform.
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birds of the same feather tweet together bayesian ideal Point Estimation using twitter data
Political Analysis, 2015Co-Authors: Pablo BarberaAbstract:Parties, candidates, and voters are becoming increasingly engaged in political conversations through the micro-blogging platform Twitter. In this paper I show that the structure of the social networks in which they are embedded has the potential to become a source of information about policy positions. Under the assumption that social networks are homophilic (McPherson et al., 2001), this is, the propensity of users to cluster along partisan lines, I develop a Bayesian Spatial Following model that scales Twitter users along a common ideological dimension based on who they follow. I apply this network-based method to estimate ideal Points for Twitter users in the US, the UK, Spain, Italy, and the Netherlands. The resulting positions of the party accounts on Twitter are highly correlated with oine measures based on their voting records and their manifestos. Similarly, this method is able to successfully classify individuals who state their political orientation publicly, and a sample of users from the state of Ohio whose Twitter accounts are matched with their voter registration history. To illustrate the potential contribution of these estimates, I examine the extent to which online behavior is polarized along ideological lines. Using the 2012 US presidential election campaign as a case study, I nd that public exchanges on Twitter take place predominantly among users with similar viewPoints.
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birds of the same feather tweet together bayesian ideal Point Estimation using twitter data
2013Co-Authors: Pablo BarberaAbstract:Political actors and citizens increasingly engage in political conversations on social media outlets such as Twitter. In this paper I show that the structure of the social networks in which they are embedded has the potential to become a source of information about policy positions. Under the assumption that social networks are homophilic, I develop a Bayesian Spatial Following model that scales Twitter users along a common ideological dimension based on who they follow. I apply this network-based method to estimate ideal Points for a large sample of Twitter users in the US, the UK, Spain, Germany, Italy, and the Netherlands. The resulting positions of the party accounts on Twitter are highly correlated with offline measures based on their voting records and their manifestos. Similarly, this method is able to successfully classify individuals who state their political orientation publicly, and a sample of users from the state of Ohio whose Twitter accounts are matched with their voter registration history. To illustrate the potential contribution of these estimates, I examine the extent to which online behavior is polarized along ideological lines. Using the 2012 US presidential election campaign as a case study, I find that public exchanges on Twitter take place predominantly among users with similar viewPoints.
