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Subhash R. Lele - One of the best experts on this subject based on the ideXlab platform.
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revisiting resource Selection Probability functions and single visit methods clarification and extensions
Methods in Ecology and Evolution, 2016Co-Authors: Péter Sólymos, Subhash R. LeleAbstract:Summary Models accounting for imperfect detection are important. Single-visit (SV) methods have been proposed as an alternative to multiple-visit methods to relax the assumption of closed population. Knape & Korner-Nievergelt (Methods in Ecology and Evolution, 2015) showed that under certain models of Probability of detection, SV methods are statistically non-identifiable leading to biased population estimates. There is a close relationship between estimation of the resource Selection Probability function (RSPF) using weighted distributions and SV methods for occupancy and abundance estimation. We explain the precise mathematical conditions needed for RSPF estimation as stated in Lele & Keim (Ecology, 87, 2006, 3021). The identical conditions that remained unstated in our papers on SV methodology are needed for SV methodology to work. We show that the class of admissible models is quite broad and does not excessively restrict the application of the RSPF or the SV methodology. To complement the work by Knape and Korner-Nievergelt, we study the performance of multiple-visit methods under the scaled logistic detection function and a much wider set of situations. In general, under the scaled logistic detection function, multiple-visit methods also lead to biased estimates. As a solution to this problem, we extend the SV methodology to a class of models that allows use of scaled Probability function. We propose a multinomial extension of SV methodology that can be used to check whether the detection function satisfies the RSPF condition or not. Furthermore, we show that if the scaling factor depends on covariates, then it can also be estimated. We argue that the instances where the RSPF condition is not satisfied are rare in practice. Hence, we disagree with the implication in Knape & Korner-Nievergelt (Methods in Ecology and Evolution, 2015) that the need for RSPF condition makes SV methodology irrelevant in practice.
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Appendix A. Parameter values and results for snowshoe hare resource Selection Probability function (RSPF) and a linear model relating the intensity of snowshoe hare tracks with snowshoe hare RSPF.
2016Co-Authors: Jonah L. Keim, Philip D. Dewitt, Subhash R. LeleAbstract:Parameter values and results for snowshoe hare resource Selection Probability function (RSPF) and a linear model relating the intensity of snowshoe hare tracks with snowshoe hare RSPF
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Revisiting resource Selection Probability functions and single-visit methods: Clarification and extensions
Methods in Ecology and Evolution, 2015Co-Authors: Péter Sólymos, Subhash R. LeleAbstract:Models accounting for imperfect detection are important. Single-visit methods have been proposed as an alternative to multiple-visits methods to relax the assumption of closed population. Knape and Korner-Nievergelt (2015) showed that under certain models of Probability of detection single-visit methods are statistically non-identifiable leading to biased population estimates. There is a close relationship between estimation of the resource Selection Probability function (RSPF) using weighted distributions and single-visit methods for occupancy and abundance estimation. We explain the precise mathematical conditions needed for RSPF estimation as stated in Lele and Keim (2006). The identical conditions, that remained unstated in our papers on single-visit methodology, are needed for single-visit methodology to work. We show that the class of admissible models is quite broad and does not excessively restrict the application of the RSPF or the single-visit methodology. To complement the work by Knape and Korner-Nievergelt, we study the performance of multiple-visit methods under the scaled logistic detection function and a much wider set of situations. In general, under the scaled logistic detection function multiple-visits methods also lead to biased estimates. As a solution to this problem, we extend the single-visit methodology to a class of models that allows use of scaled Probability function. We propose a Multinomial extension of single visit methodology that can be used to check whether the detection function satisfies the RSPF condition or not. Furthermore, we show that if the scaling factor depends on covariates, then it can also be estimated.
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A New Method for Estimation of Resource Selection Probability Function
Journal of Wildlife Management, 2009Co-Authors: Subhash R. LeleAbstract:Abstract Weighted distributions can be used to fit various forms of resource Selection Probability functions (RSPF) under the use-versus-available study design (Lele and Keim 2006). Although valid, the numerical maximization procedure used by Lele and Keim (2006) is unstable because of the inherent roughness of the Monte Carlo likelihood function. We used a combination of the methods of partial likelihood and data cloning to obtain maximum likelihood estimators of the RSPF in a numerically stable fashion. We demonstrated the methodology using simulated data sets generated under the log–log RSPF model and a reanalysis of telemetry data presented in Lele and Keim (2006) using the logistic RSPF model. The new method for estimation of RSPF can be used to understand differential Selection of resources by animals, an essential component of studies in conservation biology, wildlife management, and applied ecology.
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WEIGHTED DISTRIBUTIONS AND ESTIMATION OF RESOURCE Selection Probability FUNCTIONS
Ecology, 2006Co-Authors: Subhash R. Lele, Jonah L. KeimAbstract:Understanding how organisms selectively use resources is essential for designing wildlife management strategies. The Probability that an individual uses a given resource, as characterized by environmental factors, can be quantified in terms of the resource Selection Probability function (RSPF). The present literature on the topic has claimed that, except when both used and unused sites are known, the RSPF is non-estimable and that only a function proportional to RSPF, namely, the resource Selection function (RSF) can be estimated. This paper describes a close connection between the estimation of the RSPF and the estimation of the weight function in the theory of weighted distributions. This connection can be used to obtain fully efficient, maximum likelihood estimators of the resource Selection Probability function under commonly used survey designs in wildlife management. The method is illustrated using GPS collar data for mountain goats (Oreamnos americanus de Blainville 1816) in northwest British Columbia, Canada.
Péter Sólymos - One of the best experts on this subject based on the ideXlab platform.
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revisiting resource Selection Probability functions and single visit methods clarification and extensions
Methods in Ecology and Evolution, 2016Co-Authors: Péter Sólymos, Subhash R. LeleAbstract:Summary Models accounting for imperfect detection are important. Single-visit (SV) methods have been proposed as an alternative to multiple-visit methods to relax the assumption of closed population. Knape & Korner-Nievergelt (Methods in Ecology and Evolution, 2015) showed that under certain models of Probability of detection, SV methods are statistically non-identifiable leading to biased population estimates. There is a close relationship between estimation of the resource Selection Probability function (RSPF) using weighted distributions and SV methods for occupancy and abundance estimation. We explain the precise mathematical conditions needed for RSPF estimation as stated in Lele & Keim (Ecology, 87, 2006, 3021). The identical conditions that remained unstated in our papers on SV methodology are needed for SV methodology to work. We show that the class of admissible models is quite broad and does not excessively restrict the application of the RSPF or the SV methodology. To complement the work by Knape and Korner-Nievergelt, we study the performance of multiple-visit methods under the scaled logistic detection function and a much wider set of situations. In general, under the scaled logistic detection function, multiple-visit methods also lead to biased estimates. As a solution to this problem, we extend the SV methodology to a class of models that allows use of scaled Probability function. We propose a multinomial extension of SV methodology that can be used to check whether the detection function satisfies the RSPF condition or not. Furthermore, we show that if the scaling factor depends on covariates, then it can also be estimated. We argue that the instances where the RSPF condition is not satisfied are rare in practice. Hence, we disagree with the implication in Knape & Korner-Nievergelt (Methods in Ecology and Evolution, 2015) that the need for RSPF condition makes SV methodology irrelevant in practice.
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Revisiting resource Selection Probability functions and single-visit methods: Clarification and extensions
Methods in Ecology and Evolution, 2015Co-Authors: Péter Sólymos, Subhash R. LeleAbstract:Models accounting for imperfect detection are important. Single-visit methods have been proposed as an alternative to multiple-visits methods to relax the assumption of closed population. Knape and Korner-Nievergelt (2015) showed that under certain models of Probability of detection single-visit methods are statistically non-identifiable leading to biased population estimates. There is a close relationship between estimation of the resource Selection Probability function (RSPF) using weighted distributions and single-visit methods for occupancy and abundance estimation. We explain the precise mathematical conditions needed for RSPF estimation as stated in Lele and Keim (2006). The identical conditions, that remained unstated in our papers on single-visit methodology, are needed for single-visit methodology to work. We show that the class of admissible models is quite broad and does not excessively restrict the application of the RSPF or the single-visit methodology. To complement the work by Knape and Korner-Nievergelt, we study the performance of multiple-visit methods under the scaled logistic detection function and a much wider set of situations. In general, under the scaled logistic detection function multiple-visits methods also lead to biased estimates. As a solution to this problem, we extend the single-visit methodology to a class of models that allows use of scaled Probability function. We propose a Multinomial extension of single visit methodology that can be used to check whether the detection function satisfies the RSPF condition or not. Furthermore, we show that if the scaling factor depends on covariates, then it can also be estimated.
Jaakko Astola - One of the best experts on this subject based on the ideXlab platform.
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Boolean derivatives, weighted Chow parameters, and Selection probabilities of stack filters
IEEE Transactions on Signal Processing, 1996Co-Authors: Karen Egiazarian, Pauli Kuosmanen, Jaakko AstolaAbstract:The theory of Boolean derivatives, the activities of the arguments of a Boolean function (BF), and Chow (1961) parameters are studied from the point of view of their application in the statistical analysis of a class of nonlinear filters-stack filters. The connection between the partial derivatives of a positive BF (PBF) and the Selection probabilities of stack filters is established. The notions of the weighted activities of the variables of the PBF and weighted Chow parameters are introduced for the analysis, the computation of the joint Selection Probability matrix, and the sample Selection Probability vector of a continuous stack filter. Spectral approaches to the Selection probabilities of stack filters are derived. In particular, spectral algorithms with computational complexity O(2/sup N/), where N is the number of input samples within an input window, are given for the computation of sample Selection Probability vectors. The difference of the spectral algorithms presented from the nonspectral ones is that spectral algorithms are universal, i.e., their complexities are independent of the PBF, which is used as the base for stack filtering. They are also straightforward to implement, and fast spectral transforms exist.
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Spectral approach to Selection probabilities of stack filters
Nonlinear Image Processing VI, 1995Co-Authors: Karen Egiazarian, Pauli Kuosmanen, Jaakko Astola, Sos S. AgaianAbstract:Spectral approaches to the Selection probabilities of stack filters are derived. The spectral algorithms are given for the computation of the rank and sample Selection Probability vectors. They have computational complexity O(2N), where N is the number of input samples within the window. The main advantage of the spectral algorithms to the nonspectral ones is that spectral algorithms are universal in the sense that the complexities of these algorithms are independent on the logical function used as the base for stack filtering. They are also straightforward to implement and fast spectral transforms exist.© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
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ICIP (1) - Fault detection in stack filter circuits based on sample Selection probabilities
Proceedings of 3rd IEEE International Conference on Image Processing, 1Co-Authors: Imed Ben Dhaou, Pauli Kuosmanen, David Akopian, Jaakko AstolaAbstract:Many of the signal and image processing problems concern the suppression of the noise which is non-Gaussian and non-additive. Stack filters are nonlinear filters which are often successfully used for this kind of noise cancellation. A fault detection method is proposed for stack filters. The core of the method is the sample Selection Probability vectors of stack filters. A simple implementation for fault diagnosis is derived based on this notion.
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ICASSP - Calculation of the sample Selection probabilities of stack filters by using weighted Chow parameters
1995 International Conference on Acoustics Speech and Signal Processing, 1Co-Authors: Pauli Kuosmanen, Karen Egiazarian, Jaakko AstolaAbstract:In the present work weighted Chow parameters are developed with the aim of their application in the statistical analysis of a class of nonlinear filters, namely stack filters, which are specified by positive Boolean functions (PBF) representing the binary output at each threshold level of the continuous-valued signal. Selection probabilities of stack filters were defined based on the fact that the output of a continuous stack filter is one of the samples within the input window. The notion of weighted Chow parameters is introduced in this paper for analysis and computation of the sample Selection Probability vector of a continuous stack filter.
Pauli Kuosmanen - One of the best experts on this subject based on the ideXlab platform.
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Boolean derivatives, weighted Chow parameters, and Selection probabilities of stack filters
IEEE Transactions on Signal Processing, 1996Co-Authors: Karen Egiazarian, Pauli Kuosmanen, Jaakko AstolaAbstract:The theory of Boolean derivatives, the activities of the arguments of a Boolean function (BF), and Chow (1961) parameters are studied from the point of view of their application in the statistical analysis of a class of nonlinear filters-stack filters. The connection between the partial derivatives of a positive BF (PBF) and the Selection probabilities of stack filters is established. The notions of the weighted activities of the variables of the PBF and weighted Chow parameters are introduced for the analysis, the computation of the joint Selection Probability matrix, and the sample Selection Probability vector of a continuous stack filter. Spectral approaches to the Selection probabilities of stack filters are derived. In particular, spectral algorithms with computational complexity O(2/sup N/), where N is the number of input samples within an input window, are given for the computation of sample Selection Probability vectors. The difference of the spectral algorithms presented from the nonspectral ones is that spectral algorithms are universal, i.e., their complexities are independent of the PBF, which is used as the base for stack filtering. They are also straightforward to implement, and fast spectral transforms exist.
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Spectral approach to Selection probabilities of stack filters
Nonlinear Image Processing VI, 1995Co-Authors: Karen Egiazarian, Pauli Kuosmanen, Jaakko Astola, Sos S. AgaianAbstract:Spectral approaches to the Selection probabilities of stack filters are derived. The spectral algorithms are given for the computation of the rank and sample Selection Probability vectors. They have computational complexity O(2N), where N is the number of input samples within the window. The main advantage of the spectral algorithms to the nonspectral ones is that spectral algorithms are universal in the sense that the complexities of these algorithms are independent on the logical function used as the base for stack filtering. They are also straightforward to implement and fast spectral transforms exist.© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
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ICIP (1) - Fault detection in stack filter circuits based on sample Selection probabilities
Proceedings of 3rd IEEE International Conference on Image Processing, 1Co-Authors: Imed Ben Dhaou, Pauli Kuosmanen, David Akopian, Jaakko AstolaAbstract:Many of the signal and image processing problems concern the suppression of the noise which is non-Gaussian and non-additive. Stack filters are nonlinear filters which are often successfully used for this kind of noise cancellation. A fault detection method is proposed for stack filters. The core of the method is the sample Selection Probability vectors of stack filters. A simple implementation for fault diagnosis is derived based on this notion.
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ICASSP - Calculation of the sample Selection probabilities of stack filters by using weighted Chow parameters
1995 International Conference on Acoustics Speech and Signal Processing, 1Co-Authors: Pauli Kuosmanen, Karen Egiazarian, Jaakko AstolaAbstract:In the present work weighted Chow parameters are developed with the aim of their application in the statistical analysis of a class of nonlinear filters, namely stack filters, which are specified by positive Boolean functions (PBF) representing the binary output at each threshold level of the continuous-valued signal. Selection probabilities of stack filters were defined based on the fact that the output of a continuous stack filter is one of the samples within the input window. The notion of weighted Chow parameters is introduced in this paper for analysis and computation of the sample Selection Probability vector of a continuous stack filter.
Karen Egiazarian - One of the best experts on this subject based on the ideXlab platform.
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Boolean derivatives, weighted Chow parameters, and Selection probabilities of stack filters
IEEE Transactions on Signal Processing, 1996Co-Authors: Karen Egiazarian, Pauli Kuosmanen, Jaakko AstolaAbstract:The theory of Boolean derivatives, the activities of the arguments of a Boolean function (BF), and Chow (1961) parameters are studied from the point of view of their application in the statistical analysis of a class of nonlinear filters-stack filters. The connection between the partial derivatives of a positive BF (PBF) and the Selection probabilities of stack filters is established. The notions of the weighted activities of the variables of the PBF and weighted Chow parameters are introduced for the analysis, the computation of the joint Selection Probability matrix, and the sample Selection Probability vector of a continuous stack filter. Spectral approaches to the Selection probabilities of stack filters are derived. In particular, spectral algorithms with computational complexity O(2/sup N/), where N is the number of input samples within an input window, are given for the computation of sample Selection Probability vectors. The difference of the spectral algorithms presented from the nonspectral ones is that spectral algorithms are universal, i.e., their complexities are independent of the PBF, which is used as the base for stack filtering. They are also straightforward to implement, and fast spectral transforms exist.
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Spectral approach to Selection probabilities of stack filters
Nonlinear Image Processing VI, 1995Co-Authors: Karen Egiazarian, Pauli Kuosmanen, Jaakko Astola, Sos S. AgaianAbstract:Spectral approaches to the Selection probabilities of stack filters are derived. The spectral algorithms are given for the computation of the rank and sample Selection Probability vectors. They have computational complexity O(2N), where N is the number of input samples within the window. The main advantage of the spectral algorithms to the nonspectral ones is that spectral algorithms are universal in the sense that the complexities of these algorithms are independent on the logical function used as the base for stack filtering. They are also straightforward to implement and fast spectral transforms exist.© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
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ICASSP - Calculation of the sample Selection probabilities of stack filters by using weighted Chow parameters
1995 International Conference on Acoustics Speech and Signal Processing, 1Co-Authors: Pauli Kuosmanen, Karen Egiazarian, Jaakko AstolaAbstract:In the present work weighted Chow parameters are developed with the aim of their application in the statistical analysis of a class of nonlinear filters, namely stack filters, which are specified by positive Boolean functions (PBF) representing the binary output at each threshold level of the continuous-valued signal. Selection probabilities of stack filters were defined based on the fact that the output of a continuous stack filter is one of the samples within the input window. The notion of weighted Chow parameters is introduced in this paper for analysis and computation of the sample Selection Probability vector of a continuous stack filter.
