
Previous Article
Optimal harvesting policy for the BevertonHolt model
 MBE Home
 This Issue

Next Article
Competition for a single resource and coexistence of several species in the chemostat
An adaptive feedback methodology for determining information content in stable population studies
1.  Center for Research in Scientific Computation, North Carolina State University, Raleigh, NC 276958212 
2.  Undergraduate Research Opportunities Center (UROC), California State University, Monterey Bay, United States 
3.  Center for Research in Scientic Computation, North Carolina State University, Raleigh, NC 276958212, United States 
4.  Ecotoxicology Program, WSU Puyallup Research, Extension Center, Puyallup, WA 983714998, United States 
References:
[1] 
K. Adoteye, H. T. Banks, K. Cross, S. Eytcheson, K. B. Flores, G. A. LeBlanc, T. Nguyen, C. Ross, E. Smith, M. Stemkovski and S. Stokely, Statistical validation of structured population models for Daphnia magna, Mathematical Biosciences, 266 (2015), 7384. doi: 10.1016/j.mbs.2015.06.003. 
[2] 
K. Adoteye, H. T. Banks, K. B. Flores and G. A. LeBlanc, Estimation of timevarying mortality rates using continuous models for Daphnia magna, Applied Mathematical Letters, 44 (2015), 1216. doi: 10.1016/j.aml.2014.12.014. 
[3] 
H. T. Banks, J. E. Banks, L. K. Dick and J. D. Stark, Estimation of dynamic rate parameters in insect populations undergoing sublethal exposure to pesticides, Bulletin of Mathematical Biology, 69 (2007), 21392180. doi: 10.1007/s115380079207z. 
[4] 
H. T. Banks, J. E. Banks, R. Everett and J. Stark, An Adaptive Feedback Methodology for Determining Information Content in Population Studies, CRSCTR1512, Center for Research in Scientific Computation, N. C. State University, Raleigh, NC, November, 2015. 
[5] 
H. T. Banks, J. E. Banks, S. J. Joyner and J. D. Stark, Dynamic models for insect mortality due to exposure to insecticides, Mathematical and Computer Modeling, 48 (2008), 316332. doi: 10.1016/j.mcm.2007.10.005. 
[6] 
J. E. Banks, L. K. Dick, H. T. Banks and J. D. Stark, Timevarying vital rates in ecotoxicology: Selective pesticides and aphid population dynamics, Ecological Modeling, 210 (2008), 155160. doi: 10.1016/j.ecolmodel.2007.07.022. 
[7] 
H. T. Banks, S. Hu and W. C. Thompson, Modeling and Inverse Problems in the Presence of Uncertainty, CRC Press, New York, 2014. 
[8] 
H. T. Banks and H. T. Tran, Mathematical and Experimental Modeling of Physical and Biological Processes, CRC Press, New York, 2009. 
[9] 
J. R. Carey, Applied Demography for Biologists with Special Emphasis on Insects, Oxford University Press, Oxford, 1993. 
[10] 
V. E. Forbes and P. Calow, Is the per capita rate of increase a good measure of populationlevel effects in ecotoxicology? Environmental Toxicology and Chemistry, 18 (1999), 15441556. doi: 10.1002/etc.5620180729. 
[11] 
V. E. Forbes and P. Calow, Extrapolation in ecological risk assessment: Balancing pragmatism and precaution in chemical controls legislation, Bioscience, 52 (2002), 249257. 
[12] 
V. E. Forbes and P. Calow, Population growth rate as a basis for ecological risk assessment of toxic chemicals, Philosophical Transaction of the Royal Society, London, B: Biological Sciences, 357 (2002), 12991306. 
[13] 
N. Hanson and J. D. Stark, A comparison of simple and complex population models to reduce uncertainty in ecological risk assessments of chemicals: Example with three species of Daphnia, Ecotoxicology, 20 (2011), 12681276. doi: 10.1007/s1064601106754. 
[14] 
N. Hanson and J. D. Stark, Utility of population models to reduce uncertainty and increase value relevance in ecological risk assessments of pesticides: An example based on acute mortality data for Daphnids, Integrated Environmental Assessment and Management, 8 (2012), 262270. doi: 10.1002/ieam.272. 
[15] 
U. Hommen, J. M. Baveco, N. Galic and P. J. van den Brink, Potential application of ecological models in the European environmental risk assessment of chemicals I: review of protection goals in EU directives and regulations, Integrated Environmental Assessment and Management, 6 (2010), 325337. doi: 10.1002/ieam.69. 
[16] 
M. Kot, Elements of Mathematical Ecology, Cambridge University Press, Cambridge, 2001. doi: 10.1017/CBO9780511608520. 
[17] 
J. D. Stark and J. E. Banks, Developing Demographic Toxicity Data: Optimizing Effort for Predicting Population Outcomes, PeerJ, submitted, 2015. 
show all references
References:
[1] 
K. Adoteye, H. T. Banks, K. Cross, S. Eytcheson, K. B. Flores, G. A. LeBlanc, T. Nguyen, C. Ross, E. Smith, M. Stemkovski and S. Stokely, Statistical validation of structured population models for Daphnia magna, Mathematical Biosciences, 266 (2015), 7384. doi: 10.1016/j.mbs.2015.06.003. 
[2] 
K. Adoteye, H. T. Banks, K. B. Flores and G. A. LeBlanc, Estimation of timevarying mortality rates using continuous models for Daphnia magna, Applied Mathematical Letters, 44 (2015), 1216. doi: 10.1016/j.aml.2014.12.014. 
[3] 
H. T. Banks, J. E. Banks, L. K. Dick and J. D. Stark, Estimation of dynamic rate parameters in insect populations undergoing sublethal exposure to pesticides, Bulletin of Mathematical Biology, 69 (2007), 21392180. doi: 10.1007/s115380079207z. 
[4] 
H. T. Banks, J. E. Banks, R. Everett and J. Stark, An Adaptive Feedback Methodology for Determining Information Content in Population Studies, CRSCTR1512, Center for Research in Scientific Computation, N. C. State University, Raleigh, NC, November, 2015. 
[5] 
H. T. Banks, J. E. Banks, S. J. Joyner and J. D. Stark, Dynamic models for insect mortality due to exposure to insecticides, Mathematical and Computer Modeling, 48 (2008), 316332. doi: 10.1016/j.mcm.2007.10.005. 
[6] 
J. E. Banks, L. K. Dick, H. T. Banks and J. D. Stark, Timevarying vital rates in ecotoxicology: Selective pesticides and aphid population dynamics, Ecological Modeling, 210 (2008), 155160. doi: 10.1016/j.ecolmodel.2007.07.022. 
[7] 
H. T. Banks, S. Hu and W. C. Thompson, Modeling and Inverse Problems in the Presence of Uncertainty, CRC Press, New York, 2014. 
[8] 
H. T. Banks and H. T. Tran, Mathematical and Experimental Modeling of Physical and Biological Processes, CRC Press, New York, 2009. 
[9] 
J. R. Carey, Applied Demography for Biologists with Special Emphasis on Insects, Oxford University Press, Oxford, 1993. 
[10] 
V. E. Forbes and P. Calow, Is the per capita rate of increase a good measure of populationlevel effects in ecotoxicology? Environmental Toxicology and Chemistry, 18 (1999), 15441556. doi: 10.1002/etc.5620180729. 
[11] 
V. E. Forbes and P. Calow, Extrapolation in ecological risk assessment: Balancing pragmatism and precaution in chemical controls legislation, Bioscience, 52 (2002), 249257. 
[12] 
V. E. Forbes and P. Calow, Population growth rate as a basis for ecological risk assessment of toxic chemicals, Philosophical Transaction of the Royal Society, London, B: Biological Sciences, 357 (2002), 12991306. 
[13] 
N. Hanson and J. D. Stark, A comparison of simple and complex population models to reduce uncertainty in ecological risk assessments of chemicals: Example with three species of Daphnia, Ecotoxicology, 20 (2011), 12681276. doi: 10.1007/s1064601106754. 
[14] 
N. Hanson and J. D. Stark, Utility of population models to reduce uncertainty and increase value relevance in ecological risk assessments of pesticides: An example based on acute mortality data for Daphnids, Integrated Environmental Assessment and Management, 8 (2012), 262270. doi: 10.1002/ieam.272. 
[15] 
U. Hommen, J. M. Baveco, N. Galic and P. J. van den Brink, Potential application of ecological models in the European environmental risk assessment of chemicals I: review of protection goals in EU directives and regulations, Integrated Environmental Assessment and Management, 6 (2010), 325337. doi: 10.1002/ieam.69. 
[16] 
M. Kot, Elements of Mathematical Ecology, Cambridge University Press, Cambridge, 2001. doi: 10.1017/CBO9780511608520. 
[17] 
J. D. Stark and J. E. Banks, Developing Demographic Toxicity Data: Optimizing Effort for Predicting Population Outcomes, PeerJ, submitted, 2015. 
[1] 
Zhuoyi Xu, Yong Xia, Deren Han. On boxconstrained total least squares problem. Numerical Algebra, Control and Optimization, 2020, 10 (4) : 439449. doi: 10.3934/naco.2020043 
[2] 
Yanfei Lu, Qingfei Yin, Hongyi Li, Hongli Sun, Yunlei Yang, Muzhou Hou. Solving higher order nonlinear ordinary differential equations with least squares support vector machines. Journal of Industrial and Management Optimization, 2020, 16 (3) : 14811502. doi: 10.3934/jimo.2019012 
[3] 
Zhou Sheng, Gonglin Yuan, Zengru Cui, Xiabin Duan, Xiaoliang Wang. An adaptive trust region algorithm for largeresidual nonsmooth least squares problems. Journal of Industrial and Management Optimization, 2018, 14 (2) : 707718. doi: 10.3934/jimo.2017070 
[4] 
Chengjin Li. Parameterrelated projectionbased iterative algorithm for a kind of generalized positive semidefinite least squares problem. Numerical Algebra, Control and Optimization, 2020, 10 (4) : 511520. doi: 10.3934/naco.2020048 
[5] 
Mahendra Piraveenan, Mikhail Prokopenko, Albert Y. Zomaya. On congruity of nodes and assortative information content in complex networks. Networks and Heterogeneous Media, 2012, 7 (3) : 441461. doi: 10.3934/nhm.2012.7.441 
[6] 
C. Bonanno. The algorithmic information content for randomly perturbed systems. Discrete and Continuous Dynamical Systems  B, 2004, 4 (4) : 921934. doi: 10.3934/dcdsb.2004.4.921 
[7] 
Daniel G. Alfaro Vigo, Amaury C. Álvarez, Grigori Chapiro, Galina C. García, Carlos G. Moreira. Solving the inverse problem for an ordinary differential equation using conjugation. Journal of Computational Dynamics, 2020, 7 (2) : 183208. doi: 10.3934/jcd.2020008 
[8] 
XiaoWen Chang, David TitleyPeloquin. An improved algorithm for generalized least squares estimation. Numerical Algebra, Control and Optimization, 2020, 10 (4) : 451461. doi: 10.3934/naco.2020044 
[9] 
Yunhai Xiao, SoonYi Wu, BingSheng He. A proximal alternating direction method for $\ell_{2,1}$norm least squares problem in multitask feature learning. Journal of Industrial and Management Optimization, 2012, 8 (4) : 10571069. doi: 10.3934/jimo.2012.8.1057 
[10] 
Pedro Caro. On an inverse problem in electromagnetism with local data: stability and uniqueness. Inverse Problems and Imaging, 2011, 5 (2) : 297322. doi: 10.3934/ipi.2011.5.297 
[11] 
Victor Isakov. On uniqueness in the inverse conductivity problem with local data. Inverse Problems and Imaging, 2007, 1 (1) : 95105. doi: 10.3934/ipi.2007.1.95 
[12] 
Hassan Mohammad, Mohammed Yusuf Waziri, Sandra Augusta Santos. A brief survey of methods for solving nonlinear leastsquares problems. Numerical Algebra, Control and Optimization, 2019, 9 (1) : 113. doi: 10.3934/naco.2019001 
[13] 
YaXiang Yuan. Recent advances in numerical methods for nonlinear equations and nonlinear least squares. Numerical Algebra, Control and Optimization, 2011, 1 (1) : 1534. doi: 10.3934/naco.2011.1.15 
[14] 
Mila Nikolova. Analytical bounds on the minimizers of (nonconvex) regularized leastsquares. Inverse Problems and Imaging, 2008, 2 (1) : 133149. doi: 10.3934/ipi.2008.2.133 
[15] 
Yanyan Hu, Fubao Xi, Min Zhu. Least squares estimation for distributiondependent stochastic differential delay equations. Communications on Pure and Applied Analysis, 2022, 21 (4) : 15051536. doi: 10.3934/cpaa.2022027 
[16] 
Subrata Dasgupta. Disentangling data, information and knowledge. Big Data & Information Analytics, 2016, 1 (4) : 377389. doi: 10.3934/bdia.2016016 
[17] 
Suman Kumar Sahoo, Manmohan Vashisth. A partial data inverse problem for the convectiondiffusion equation. Inverse Problems and Imaging, 2020, 14 (1) : 5375. doi: 10.3934/ipi.2019063 
[18] 
Francis J. Chung. Partial data for the NeumannDirichlet magnetic Schrödinger inverse problem. Inverse Problems and Imaging, 2014, 8 (4) : 959989. doi: 10.3934/ipi.2014.8.959 
[19] 
Valter Pohjola. An inverse problem for the magnetic Schrödinger operator on a half space with partial data. Inverse Problems and Imaging, 2014, 8 (4) : 11691189. doi: 10.3934/ipi.2014.8.1169 
[20] 
Li Liang. Increasing stability for the inverse problem of the Schrödinger equation with the partial Cauchy data. Inverse Problems and Imaging, 2015, 9 (2) : 469478. doi: 10.3934/ipi.2015.9.469 
2018 Impact Factor: 1.313
Tools
Metrics
Other articles
by authors
[Back to Top]