scholarly journals The Limiting Distribution of the Maximum Rank Correlation Estimator

Econometrica ◽  
1993 ◽  
Vol 61 (1) ◽  
pp. 123 ◽  
Author(s):  
Robert P. Sherman
Keyword(s):  
Rank Correlation ◽  
Limiting Distribution ◽  
Maximum Rank ◽  
Distribution Of The Maximum

Computation of the maximum rank correlation estimator

1999 ◽  
Vol 62 (3) ◽  
pp. 279-285 ◽  
Author(s):  
Jason Abrevaya
Keyword(s):  
Rank Correlation ◽  
Maximum Rank

The exponential rate of convergence of the distribution of the maximum of a random walk

1975 ◽  
Vol 12 (02) ◽  
pp. 279-288 ◽  
Author(s):  
N. Veraverbeke ◽  
J. L. Teugels
Keyword(s):  
Distribution Function ◽  
Random Walk ◽  
Rate Of Convergence ◽  
Exponential Convergence ◽  
Random Variables ◽  
Limiting Distribution ◽  
Partial Sums ◽  
Exponential Rate ◽  
Distribution Of The Maximum ◽  
Exponential Rate Of Convergence

Let Gn (x) be the distribution function of the maximum of the successive partial sums of independent and identically distributed random variables and G(x) its limiting distribution function. Under conditions, typical for complete exponential convergence, the decay of Gn (x) — G(x) is asymptotically equal to c.H(x)n −3/2 γn as n → ∞ where c and γ are known constants and H(x) is a function solely depending on x.

scholarly journals Exact Computation of Maximum Rank Correlation Estimator

2021 ◽  
Author(s):  
Youngki Shin ◽  
Zvezdomir Todorov
Keyword(s):  
Optimization Problem ◽  
Original Problem ◽  
Rank Correlation ◽  
Predictive Performance ◽  
Tail Probability ◽  
Performance Measure ◽  
Mixed Integer ◽  
Maximum Rank ◽  
Computation Algorithm ◽  
Indicator Functions

Abstract In this paper we provide a computation algorithm to get a global solution for the maximum rank correlation estimator using the mixed integer programming (MIP) approach. We construct a new constrained optimization problem by transforming all indicator functions into binary parameters to be estimated and show that it is equivalent to the original problem. We also consider an application of the best subset rank prediction and show that the original optimization problem can be reformulated as MIP. We derive the non-asymptotic bound for the tail probability of the predictive performance measure. We investigate the performance of the MIP algorithm by an empirical example and Monte Carlo simulations.

The exponential rate of convergence of the distribution of the maximum of a random walk

1975 ◽  
Vol 12 (2) ◽  
pp. 279-288 ◽  
Author(s):  
N. Veraverbeke ◽  
J. L. Teugels
Keyword(s):  
Distribution Function ◽  
Random Walk ◽  
Rate Of Convergence ◽  
Exponential Convergence ◽  
Random Variables ◽  
Limiting Distribution ◽  
Partial Sums ◽  
Exponential Rate ◽  
Distribution Of The Maximum ◽  
Exponential Rate Of Convergence

Let Gn(x) be the distribution function of the maximum of the successive partial sums of independent and identically distributed random variables and G(x) its limiting distribution function. Under conditions, typical for complete exponential convergence, the decay of Gn(x) — G(x) is asymptotically equal to c.H(x)n−3/2γn as n → ∞ where c and γ are known constants and H(x) is a function solely depending on x.

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