A note on the exact computation of the Student Snedecor and sample correlation coefficient distribution functions

2002 ◽  
Vol 51 (1) ◽  
pp. 105-110 ◽  
Author(s):  
Lucio Barabesi ◽  
Luigi Greco
Keyword(s):  
Correlation Coefficient ◽  
Distribution Functions ◽  
Exact Computation ◽  
Coefficient Distribution ◽  
Sample Correlation

scholarly journals On the Bivariate Nakagami-Lognormal Distribution and Its Correlation Properties

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Juan Reig ◽  
Lorenzo Rubio ◽  
Vicent M. Rodrigo-Peñarrocha
Keyword(s):  
Standard Deviation ◽  
Probability Density Function ◽  
Correlation Coefficient ◽  
Lognormal Distribution ◽  
Joint Probability ◽  
Joint Probability Density Function ◽  
Coefficient Distribution ◽  
Sample Correlation ◽  
Correlation Properties ◽  
Joint Probability Density

The bivariate Nakagami-lognormal distribution used to model the composite fast fading and shadowing has been examined exhaustively. In particular, we have derived the joint probability density function, the cross-moments, and the correlation coefficient in power terms. Also, two procedures to generate two correlated Nakagami-lognormal random variables are described. These procedures can be used to evaluate the robustness of the sample correlation coefficient distribution in both macro- and microdiversity scenarios. It is shown that the bias and the standard deviation of this sample correlation coefficient are substantially high for large shadowing standard deviations found in wireless communication measurements, even if the number of observations is considerable.

Using Incidence Sampling to Estimate Covariances

1979 ◽  
Vol 4 (1) ◽  
pp. 41-58 ◽  
Author(s):  
Thomas R. Knapp
Keyword(s):  
Missing Data ◽  
Correlation Coefficient ◽  
Sample Covariance ◽  
Sample Correlation

This paper is an attempt to illustrate the generality of incidence sampling for estimating a parameter whose estimator preserves the unbiasedness of generalized symmetric means, a property which the sample covariance possesses but which the sample correlation coefficient does not. The problem of missing data is also addressed.

On the Distribution of the Sample Correlation Coefficient

1992 ◽  
pp. 466-469
Author(s):  
N. N. Mikhail ◽  
F. A. Chimenti ◽  
J. D. Kidder
Keyword(s):  
Correlation Coefficient ◽  
Sample Correlation

LSTUR regression theory and the instability of the sample correlation coefficient between financial return indices

2020 ◽  
Author(s):  
Tim Ginker ◽  
Offer Lieberman
Keyword(s):  
Correlation Coefficient ◽  
Unit Root ◽  
Asymptotic Theory ◽  
Financial Return ◽  
Spurious Regression ◽  
Root Model ◽  
Sampling Window ◽  
Sample Correlation ◽  
Underlying Processes ◽  
Regression Theory

Summary It is well known that the sample correlation coefficient between many financial return indices exhibits substantial variation on any reasonable sampling window. This stylised fact contradicts a unit root model for the underlying processes in levels, as the statistic converges in probability to a constant under this modeling scheme. In this paper, we establish asymptotic theory for regression in local stochastic unit root (LSTUR) variables. An empirical application reveals that the new theory explains very well the instability, in both sign and scale, of the sample correlation coefficient between gold, oil, and stock return price indices. In addition, we establish spurious regression theory for LSTUR variables, which generalises the results known hitherto, as well as a theory for balanced regression in this setting.

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