Refined Large Deviation Limit Theorems
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A01=Vladimir Vinogradov
advanced probability methods
Asymptotic Behavior
Asymptotic Expansion
Asymptotic Expansions of the Probabilities of Large Deviations and Non-Uniform Estimates of Remainders in CLT
Asymptotic Expansions Taking into Account the Cases when the Number of Summands Comparable with the Sum Does not Exceed a Fixed Integer
Asymptotic Expansions Taking into Account the Cases when the Number of Summands Comparable with the Sum is Less than or Equal to Two
Author_Vladimir Vinogradov
Category=PBT
Chebyshev Inequality
Common Distribution Function
Distribution Function
Edgeworth Expansion
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
Exact Asymptotics
Exponential Moments
Finite Absolute Moment
Induction Step
Large Deviation Principle
Large Deviations for I.I.D. Random Sums When Cramer's Condition Is Fulfilled Only on a Finite Interval.
Left Hand Tail
Limit Theorems on Large Deviations for Order Statistics.
Martingale Methods
Non-negative Integer
order statistics analysis
Polar Types
Power Tails
probability limit theorem applications
probability theory
Random Sums
random variable modeling
Rightmost Expression
Rightmost Term
Stationary Phase Method
statistical inference
Stochastic Exponential
stochastic processes
Subexponential Distributions
Tail Approximation
Uniform Estimate
Weak Convergence
Product details
- ISBN 9780582254992
- Weight: 385g
- Dimensions: 174 x 246mm
- Publication Date: 14 Nov 1994
- Publisher: Taylor & Francis Ltd
- Publication City/Country: GB
- Product Form: Hardback
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This is a developing area of modern probability theory, which has applications in many areas. This volume is devoted to the systematic study of results on large deviations in situations where Cramér's condition on the finiteness of exponential moments may not be satisfied
Vinogradov\, Vladimir
