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Application domains for the Delta method [PDF]
Statistics, 2013 The Delta method uses truncated Lagrange expansions of statistics to obtain approximations to their distributions. In this paper, we consider statistics Y=g(μ+X), where X is any random vector. We obtain domains 𝒟 such that, when μ∈𝒟, we may apply the distribution derived from the Delta method.
Celia Nunes +2 more
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A History of the Delta Method and Some New Results
Sankhya B, 2023zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Anil K Bera +2 more
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Delta function expansions, complex delta functions and the steepest descent method
American Journal of Physics, 1993Expansion of concentrated functions in terms of a delta function series is discussed with examples. The delta function is extended into the complex plane in the limit of the analytic Gaussian function. It is demonstrated that problems normally handled with the steepest descent method can be simply expressed as an integration of the delta function in ...
Ismo V Lindell
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A smoothing technique for discrete delta functions with application to immersed boundary method in moving boundary simulations [PDF]
Journal of Computational Physics, 2009 The effects of complex boundary conditions on flows are represented by a volume force in the immersed boundary methods. The problem with this representation is that the volume force exhibits non-physical oscillations in moving boundary simulations.
Xiaolei Yang, Xing Zhang
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SSRN Electronic Journal, 2015
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Hong, Han, Li, Jessie
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Hong, Han, Li, Jessie
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1996
After giving the general principle of the delta-method, we consider the special case of Gaussian limits and the “conditional” delta-method, which applies to the bootstrap. The chapter closes with a large number of examples.
Aad W. van der Vaart, Jon A. Wellner
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After giving the general principle of the delta-method, we consider the special case of Gaussian limits and the “conditional” delta-method, which applies to the bootstrap. The chapter closes with a large number of examples.
Aad W. van der Vaart, Jon A. Wellner
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Who Invented the Delta Method, Really?
The Mathematical Intelligencer, 2020zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Delta Method and Moment Convergence
AIP Conference Proceedings, 2010Statistics, either univariate or multivariate, are usually given by well‐behaved functions. This fact is used to obtain limit distributions for multivariate statistics whose components are given by asymptotically linear functions (see [1]). These results are then extended to the moments of distributions.
Miguel Fonseca +4 more
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The American Statistician, 1992
Abstract The delta method is an intuitive technique for approximating the moments of functions of random variables. This note reviews the delta method and conditions under which delta-method approximate moments are accurate.
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Abstract The delta method is an intuitive technique for approximating the moments of functions of random variables. This note reviews the delta method and conditions under which delta-method approximate moments are accurate.
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1980
This chapter is highly technical and is included to show that the approximations for moments of errors in data (chapter 3) and in allocations (chapters 4 and 5) can be developed rigorously. The reader is encouraged to skip this chapter for now and return to it if motivated by a desire to verify approximations developed in Later chapters.
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This chapter is highly technical and is included to show that the approximations for moments of errors in data (chapter 3) and in allocations (chapters 4 and 5) can be developed rigorously. The reader is encouraged to skip this chapter for now and return to it if motivated by a desire to verify approximations developed in Later chapters.
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