Expansions and Asymptotics for Statistics

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A01=Christopher G. Small
advanced asymptotic analysis techniques
approximation
asymptotic analysis
Asymptotic Expansion
Asymptotic Normality
Asymptotic Series
Asymptotically Unbiased
asymptotics
Author_Christopher G. Small
Category=PBT
Cauchy Hadamard Formula
Continued Fraction
Continued Fraction Expansion
delta method
Distribution Functions
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
estimator
Euler Maclaurin Formula
expansions
function
Laplace Approximation
Laplace Expansion
likelihood
likelihood estimation
Local Asymptotic
Maple
maximum
Maximum Likelihood Estimator
moment-generating
Pade approximations
Partial Sums
Power Series
probability theory
random
Random Variables
Random Variables Xn
Remainder Term
Saddle Point Method
saddle-point approximation
series
series convergence
Series Expansions
Slutsky's Theorem
Slutsky’s Theorem
statistical inference
stirling's
Stirling's Approximation
Stirling’s Approximation
Symmetric Stable Distributions
Taylor series
UMVUE
variable
variance stabilization

Product details

  • ISBN 9781584885900
  • Weight: 830g
  • Dimensions: 156 x 234mm
  • Publication Date: 07 May 2010
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
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Asymptotic methods provide important tools for approximating and analysing functions that arise in probability and statistics. Moreover, the conclusions of asymptotic analysis often supplement the conclusions obtained by numerical methods. Providing a broad toolkit of analytical methods, Expansions and Asymptotics for Statistics shows how asymptotics, when coupled with numerical methods, becomes a powerful way to acquire a deeper understanding of the techniques used in probability and statistics.

The book first discusses the role of expansions and asymptotics in statistics, the basic properties of power series and asymptotic series, and the study of rational approximations to functions. With a focus on asymptotic normality and asymptotic efficiency of standard estimators, it covers various applications, such as the use of the delta method for bias reduction, variance stabilisation, and the construction of normalising transformations, as well as the standard theory derived from the work of R.A. Fisher, H. Cramér, L. Le Cam, and others. The book then examines the close connection between saddle-point approximation and the Laplace method. The final chapter explores series convergence and the acceleration of that convergence.

Christopher G. Small is a professor in the Department of Statistics and Actuarial Science at the University of Waterloo in Ontario, Canada.

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