# Delta method to assess the confidence and prediction intervals

#### Jpalbae

##### New Member
I want to calculate the table of confidence and prediction intervals for a custom Cumulative Distribution Function or CDF, and I am following the forums and articles aid.

My major cuestions that I repeat bellow are these:

1.- I had to obtain the ACOV matrix and I use the inverse of the Information matrix for the Maximum Likelihood method (-inverse of the Fisher observed information matrix)number of data regarding the articles and forum revised. s this a correct way?

2.- When multiplying my vector or 1x1 matrix, as it is the partial derivate on only one variable, by the ACOV 4x4 matrix the software gives me another matrix, as if one multiplies a number times a matrix. Instead, we can obtain a simple number by adding three Zeros to the gradient vector, but I think that this is not correct. Then, what can we do if the number of columns in our vector is different that the number of rows in our ACOV matrix?.

I attached some concepts from the theory in the file "1.jpg".

This is the standard method that I want to apply in Mathematica, as we can find out in TableCurve 2 D or other software (see 2.jpg attached).

I put here my DELTA METHOD TO OBTAIN THE VARIANCE OF A FUNCTION STEP BY STEP AND THEN THEIR CONFIDENCE AND PREDICTION INTERVALS OR BANDS: It seems that the key to carry out this method is in find the variance of the function that give us the prediction values*) I find an example where I could test a part of the procces and I develop following:*) Example http://www.montana.edu/rotella/502/DeltaExample.pdf in the case of female sex only. (See equation to the variance of a arbitrary function in the file 3.jpg attached).

Code:
VectorGrad = {1, -1, Li};
ACOVMatrix = {{0.04080, 0.0044646, -0.0054777}, {0.0044646082,
0.0438542, -0.0127939}, {-0.0054777, -0.0127939, 0.0486278733}};
(*Applying the dot product to obtain the variance of the function*)
vector to the right side and transposes it automatically*)

(*The function to  female rate,i.e., with sex=-1.*)

fem = Exp[-0.416831 + 0.540171 + 0.564244 Li]/(1 +
Exp[-0.416831 + 0.540171 + 0.564244 Li]);
uc = -0.416831 + 0.540171 + 0.564244 Li + 1.96 Sqrt[Var];
uctran = Exp[uc]/(1 + Exp[uc]);
ic = -0.416831 + 0.540171 + 0.564244 Li - 1.96 Sqrt[Var];
ictran = Exp[ic]/(1 + Exp[ic]);
Show[Plot[uctran, {Li, -3, 3}], Plot[ictran, {Li, -3, 3}],
Plot[fem, {Li, -3, 3}], PlotRange -> {{-3, 3}, {0, 1}},
AxesOrigin -> {-3, 0}, Frame -> True,
FrameStyle -> Directive[Orange, 12]]
Code:
acov[data_, dist_, paramlist_, mleRule_] := Block[{len, infmat, cov}, len = Length[data]; infmat = -D[LogLikelihood[dist, data], {paramlist, 2}]/len /. mleRule; cov = Inverse[infmat]];
Define data and distribution.

Code:
data = {31, 46, 70, 87, 87, 93, 114, 128, 133, 134, 143, 155, 161, 161, 163, 177, 181, 207, 207, 226, 302, 315, 319, 347, 347, 362, 375, 377, 413, 440, 447, 461, 464, 511, 524, 556, 800, 860, 880, 954, 5200, 12000}
Distribution fitting.The distribution is devined as a CDF for a range x - infinity. To apply the MLE and obtain the paramters I gave initial values close to the final values that I knew from the fitting with another software.

Code:
dist = ProbabilityDistribution[{"CDF",
Exp[-a Exp[-x b] - c Exp[-x d]]}, {x, 0, Infinity},
Assumptions -> {{0 < a < 10}, {0 < b < 1}, {0 < c < 10}, {0 < d <
1}}];
res = FindDistributionParameters[data,
dist, {{a, 3.8}, {b, 0.006}, {c, 0.08}, {d, 0.0002}}]
The final parameters are : {a -> 3.7607209932409895, b -> 0.006209266160285994, c -> 0.0617315221225997, d -> 0.00014543291830692012}

To obtain the ACOV we can use the acov function.

Code:
asincov = acov[data, dist, {a, b, c, d}, res];
2. To obtain the variance - covariance parameters matrix (G' Transosed * Var * G'): How to compute prediction bands for non-linear regression? - Cross Validated. Mathematica automatically transposes the third matrix when detecting that is the same as the first one.

Code:
Varian = grad.asincov.grad;
One can test the variance by pasing any value.

Code:
Varian /. {x -> 1};
Following with the assesment of the confidence and prediction bands as we can see for example in the link How to compute prediction bands for non-linear regression? - Cross Validated, we have to evaluate the Critical t- Student value for confindence level, for example 95 %, and 42 - 4 = 38 degrees of freedom, in the case of 4 parameters and 42 data I sopose this is 38. That is, the critical t (N - 4, 0.05) = t(38, 0.05), is this correct?.

In Mathematica this is tantamount to the quantile for a t with 38 degree of fit and 0.95 confidence level:

Code:
Quantile[StudentTDistribution[41], 0.95];
Finally, we have to subtitute obtain sqrt (Varian) in the case of confidence intervals, or sqrt (1 + Varian) in the case of prediction intervals, as well as to assess the MSE to complete the formulas.