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Uncertainty in fall time surrogate Prediction variance vs. data sensitivity – Non-uniform noise – Example 3.2.1 Uncertainty in fall time data Bootstrapping.

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Presentation on theme: "Uncertainty in fall time surrogate Prediction variance vs. data sensitivity – Non-uniform noise – Example 3.2.1 Uncertainty in fall time data Bootstrapping."— Presentation transcript:

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2 Uncertainty in fall time surrogate Prediction variance vs. data sensitivity – Non-uniform noise – Example Uncertainty in fall time data Bootstrapping – Estimating accuracy of statistics

3 Linear Regression Functional form For linear approximation Define then Regression coefficients Altogether Differentiate with respect to ith component of y.

4 Example Given data Linear fit X-2012 Y

5 Prediction variance with variable noise Prediction variance based on assumptions on noise Variance of surrogate prediction Allows different variances. Note that with different variances better to use weighted least squares.

6 Comparison for example at x=3 Prediction variance (surprisingly small, why?) Variance of prediction If all data variances are the same, check you get the same If not, variance of y 5 is most important

7 Bootstrapping When we calculate statistics from random data bootstrapping can provide error estimates. If we had multiple samples we can use them to estimate the error in the computation. With bootstrapping we perform the amazing feat of getting the error from a single sample. This is done by resampling with replacement the same data. We draw a samples from the original data without removing it so that the new sample may have repetitions. We repeat for many bootstrap samples to get a distribution of the statistic of interest.

8 Example with sample mean x=randn(1,10) x = bootsam(:,1:5) ans = Each column contains the indices of one boot strap sample. For example, the last column indicates that we drew x(2)= four times, x(6) twice, along with x(3), x(5), x(9), and x(10). What is the probability of getting no repetitions?

9 Matlab bootstrp routine bootstat = bootstrp(nboot,bootfun,d1,...) draws nboot bootstrap data samples, computes statistics on each sample using bootfun, and returns the results in the matrix bootstat. bootfun is a function handle specified Each row of bootstat contains the results of applying bootfun to one bootstrap sample. [bootstat,bootsam] = bootstrp(...) returns an n-by- nboot matrix of bootstrap indices, bootsam. Each column in bootsam contains indices of the values that were drawn from the original data sets to constitute the corresponding bootstrap sample

10 Statistics for sample mean mean(x) = mean(bootstat)= std(x) = std(bootstat)= In this case we know that the standard deviation of the mean is the native standard deviation divided by the square root of the sample size, or about 0.56 In other cases we may not have a formula. May use bootstrapping to estimate accuracy of probability

11 Sample standard deviation mean(bootstat)= std(bootstat)= Check ratio a=randn(10,10000); s=std(a); mean(s) = std(s)= Bootstrap ratio is 0.208, actual ratio 0.237

12 Exercise The variables x and y are normally distributed with N(0,1) marginal distributions and a correlation coefficient of Generate a sample of 10 pairs and use bootstrap to estimate the accuracy of the correlation coefficient you obtain from the sample. 2.Compare to the accuracy you can get from a formula or by repeating step 1 many times.


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