Limit Measures Arising in the Asymptotic Theory of Symmetric Groups, II

1978 ◽  
Vol 23 (1) ◽  
pp. 36-49 ◽  
Author(s):  
A. M. Vershik ◽  
A. A. Shmidt
Keyword(s):  
Asymptotic Theory ◽  
Symmetric Groups

scholarly journals Asymptotics of Young Diagrams and Hook Numbers

10.37236/1307 ◽  
1997 ◽  
Vol 4 (1) ◽  
Author(s):  
Amitai Regev ◽  
Anatoly Vershik
Keyword(s):  
Asymptotic Theory ◽  
Asymptotic Methods ◽  
Symmetric Groups ◽  
Schur Functions ◽  
Young Diagrams ◽  
Arithmetic Properties ◽  
Hook Formula ◽  
Hook Numbers

Asymptotic calculations are applied to study the degrees of certain sequences of characters of symmetric groups. Starting with a given partition $\mu$, we deduce several skew diagrams which are related to $\mu$. To each such skew diagram there corresponds the product of its hook numbers. By asymptotic methods we obtain some unexpected arithmetic properties between these products. The authors do not know "finite", nonasymptotic proofs of these results. The problem appeared in the study of the hook formula for various kinds of Young diagrams. The proofs are based on properties of shifted Schur functions, due to Okounkov and Olshanski. The theory of these functions arose from the asymptotic theory of Vershik and Kerov of the representations of the symmetric groups.

The Asymptotic Theory of the Kakwani Class of Poverty Measures

2007 ◽  
Author(s):  
Gane Samb Lo ◽  
Serigne Touba Sall ◽  
Cheikh Tidiane Seck
Keyword(s):  
Asymptotic Theory ◽  
Poverty Measures

Standard Asymptotic Theory

2017 ◽  
Author(s):  
Russell Cheng
Keyword(s):  
Asymptotic Theory ◽  
Asymptotic Variance ◽  
Statistical Significance ◽  
Regularity Conditions ◽  
Parameter Estimates ◽  
Asymptotic Results ◽  
Ml Estimation ◽  
True Parameter ◽  
Log Likelihood ◽  
Ml Estimator

This book relies on maximum likelihood (ML) estimation of parameters. Asymptotic theory assumes regularity conditions hold when the ML estimator is consistent. Typically an additional third derivative condition is assumed to ensure that the ML estimator is also asymptotically normally distributed. Standard asymptotic results that then hold are summarized in this chapter; for example, the asymptotic variance of the ML estimator is then given by the Fisher information formula, and the log-likelihood ratio, the Wald and the score statistics for testing the statistical significance of parameter estimates are all asymptotically equivalent. Also, the useful profile log-likelihood then behaves exactly as a standard log-likelihood only in a parameter space of just one dimension. Further, the model can be reparametrized to make it locally orthogonal in the neighbourhood of the true parameter value. The large exponential family of models is briefly reviewed where a unified set of regular conditions can be obtained.

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