Template:Lognormal distribution rank regression on Y: Difference between revisions

From ReliaWiki
Jump to navigation Jump to search
No edit summary
(Redirected page to The Lognormal Distribution)
 
(One intermediate revision by one other user not shown)
Line 1: Line 1:
===Rank Regression on Y===
#REDIRECT [[The Lognormal Distribution]]
Performing a rank regression on Y requires that a straight line be fitted to a set of data points such that the sum of the squares of the vertical deviations from the points to the line is minimized.
 
The least squares parameter estimation method, or regression analysis, was discussed in [[Parameter Estimation]] Chapter and the following equations for regression on Y were derived, and are again applicable:
 
::<math>\hat{a}=\bar{y}-\hat{b}\bar{x}=\frac{\underset{i=1}{\overset{N}{\mathop{\sum }}}\,{{y}_{i}}}{N}-\hat{b}\frac{\underset{i=1}{\overset{N}{\mathop{\sum }}}\,{{x}_{i}}}{N}</math>
 
and:
 
::<math>\hat{b}=\frac{\underset{i=1}{\overset{N}{\mathop{\sum }}}\,{{x}_{i}}{{y}_{i}}-\tfrac{\underset{i=1}{\overset{N}{\mathop{\sum }}}\,{{x}_{i}}\underset{i=1}{\overset{N}{\mathop{\sum }}}\,{{y}_{i}}}{N}}{\underset{i=1}{\overset{N}{\mathop{\sum }}}\,x_{i}^{2}-\tfrac{{{\left( \underset{i=1}{\overset{N}{\mathop{\sum }}}\,{{x}_{i}} \right)}^{2}}}{N}}</math>
 
In our case the equations for  <math>{{y}_{i}}</math>  and <math>x_{i}</math> are:
 
::<math>{{y}_{i}}={{\Phi }^{-1}}\left[ F(t_{i}^{\prime }) \right]</math>
 
and:
 
::<math>{{x}_{i}}=t_{i}^{\prime }</math>
 
where the  <math>F(t_{i}^{\prime })</math>  is estimated from the median ranks. Once  <math>\widehat{a}</math>  and  <math>\widehat{b}</math>  are obtained, then  <math>\widehat{\sigma }</math>  and  <math>\widehat{\mu }</math>  can easily be obtained from the above equations.
 
'''The Correlation Coefficient'''
 
The estimator of  <math>\rho </math>  is the sample correlation coefficient,  <math>\hat{\rho }</math> , given by:
 
::<math>\hat{\rho }=\frac{\underset{i=1}{\overset{N}{\mathop{\sum }}}\,({{x}_{i}}-\overline{x})({{y}_{i}}-\overline{y})}{\sqrt{\underset{i=1}{\overset{N}{\mathop{\sum }}}\,{{({{x}_{i}}-\overline{x})}^{2}}\cdot \underset{i=1}{\overset{N}{\mathop{\sum }}}\,{{({{y}_{i}}-\overline{y})}^{2}}}}</math>
 
 
'''Example 2:'''
{{Example: Lognormal Distribution RRY}}

Latest revision as of 03:22, 13 August 2012