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Part of the book series: Springer Series in Statistics ((SSS))

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Abstract

Many nonparametric estimation problems can be regarded as involving estimation of infinite dimensional parameters. This is the situation, for example, in the neuronal membrane potential model \( dV(t) = ( - pV(t) + \theta (t))dt + dM(t) \) discussed earlier in Section 2.6 with parameters held constant, if changes in θ(t) over the recording interval 0 ≤ tT are important and the function needs to be estimated. Both the situation where n replica trajectories of {V(t), 0 ≤ tT are observed and n → ∞ or one trajectory is observed and T → ∞ are of interest but most of the established results deal with the former case.

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© 1997 Springer-Verlag New York, Inc.

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(1997). Infinite Dimensional Problems. In: Heyde, C.C. (eds) Quasi-Likelihood and its Application. Springer Series in Statistics. Springer, New York, NY. https://doi.org/10.1007/0-387-22679-6_10

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  • DOI: https://doi.org/10.1007/0-387-22679-6_10

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-98225-0

  • Online ISBN: 978-0-387-22679-8

  • eBook Packages: Springer Book Archive

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