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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.
Malabika Koley, Anil Bera, Bera Anil K
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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.
Gary W Oehlert
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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.
Gary W Oehlert
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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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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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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Application domains for the Delta method
Statistics, 2013The 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.
Nunes, Célia +2 more
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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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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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