[R] Probability distribution of fitted gaussian distribution.

[David Winsemius]

On Jan 3, 2015, at 9:27 AM, Jacob Varughese wrote:

> Hi,
> 
> I have a discrete set of data on the returns for 3 indices with 206 data
> points. Since the number of points is less it doesnt exact look like a
> gaussian distribution.
> 
> I wanted to fit the data to a gaussian distribution and have used the
> fitdist function and have gotten the plots and the mean and sd estimates
> for the gaussian that fits my data.
> 
> What I then want to do is to get a U=F(x) where U is the uniform variable
> corresponding to the CDF function applied on the fitted theoritical CDF
> curve. How can I get that?
> 
> 
> Equivalent data that I find in matlab. Here the ksdensity gives an array of
> f and xi values and I could use the f values for my usage. But I am trying
> to work it out in R. The steps that I am going through in R are below. I
> have also attached the input sheet that I am using for the indices. Sorry
> in advance, case its a dumb one.
> 
> Estimate Density
> 
> Generate a sample data set from a mixture of two normal distributions.
> 
> rng default  % for reproducibility
> x = [randn(30,1); 5+randn(30,1)];
> 
> Plot the estimated density.
> 
> [f,xi] = ksdensity(x);
> figure
> plot(xi,f);
> 
> Steps that I am following.
> 
> # Reading and finding the returns for 3 indices.
> CDSPrices<-read.csv("CDS.csv")
> numRows=nrow(CDSPrices)
> CDSReturnsN225=CDSPrices$N225[2:numRows]/CDSPrices$N225[1:numRows-1]-1
> CDSReturnsSPX=CDSPrices$SPX[2:numRows]/CDSPrices$SPX[1:numRows-1]-1
> CDSReturnsIBOVESPA=CDSPrices$IBOVESPA[2:numRows]/CDSPrices$IBOVESPA[1:numRows-1]-1
> CDS_Returns<-cbind(CDSReturnsN225,CDSReturnsSPX,CDSReturnsIBOVESPA)
> 
> # Using fitdist to fit a gaussian distribution onto the discrete empirical
> data I have.
> library(fitdistrplus)
> fittedNormal<-fitdist(CDS_Returns[,1],"norm")
> plot(fittedNormal)
> 
> 
>> fittedNormal[]
> $estimate
>        mean           sd
> -0.002035951  0.028654032
> 
> $method
> [1] "mle"
> 
> $sd
>       mean          sd
> 0.001996421 0.001403953

Wouldn't you get pretty much the same result from:

 list( mean=mean(CDS_Returns[,1], sd=sd(CDS_Returns[,1]) )  # ?

... since the sample mean is identical to the MLE estimate and the sample SD is at worst only different by a factor of N/(N-1).

And for what you are calling " U=F(x) where U is the uniform variable
corresponding to the CDF function applied on the fitted theoritical CDF
curve", wouldn't that just involve the use of the `qnorm` function?


> 
> Reference
> 
> http://cran.r-project.org/web/packages/fitdistrplus/fitdistrplus.pdf  ~
> Page 15
> 
> 
> -- 
> *Jacob Varughese*
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David Winsemius
Alameda, CA, USA