StatBootstrap
The StatBootstrap class is used to create bootstrap samples and compute statistics based on these pseudo-samples. Given a sample y from a population (typically unknown), create several pseudo-samples (bootstrap samples). This allows confidence intervals to be created with requiring distribution assumptions, such as the data are Normally distributed.
Value parameters
- stream
-
the random number stream to use
- unbiased
-
whether the estimators are restricted to be unbiased
- y
-
the original sample of data
Attributes
- Graph
-
- Supertypes
Members list
Value members
Concrete methods
Compute the bootstrap confidence interval (lo, hi) for the given confidence level using the bootstrap percentile method.
Compute the bootstrap confidence interval (lo, hi) for the given confidence level using the bootstrap percentile method.
Value parameters
- p_
-
the confidence level
Attributes
- See also
-
stat.rutgers.edu/home/mxie/RCPapers/bootstrap.pdf
Compute the bootstrap confidence interval half-width (ihw) for the given confidence level using the t-distribution. Assumes the data follows a Normal distribution.
Compute the bootstrap confidence interval half-width (ihw) for the given confidence level using the t-distribution. Assumes the data follows a Normal distribution.
Value parameters
- p_
-
the confidence level
Attributes
Compute/estimate the bootstrap sample mean.
Compute/estimate the bootstrap sample mean.
Attributes
Compute/estimate the bootstrap sample standard deviation.
Compute/estimate the bootstrap sample standard deviation.
Attributes
Compute/estimate the bootstrap sample variance.
Compute/estimate the bootstrap sample variance.
Attributes
Make/generate n bootstrap samples (resampling with replacement).
Make/generate n bootstrap samples (resampling with replacement).
Value parameters
- n
-
the number of sample to make
Attributes
Generate a row of bootstrap statistical results as a string.
Generate a row of bootstrap statistical results as a string.
Attributes
Inherited methods
Compute the confidence interval half-width for the given confidence level using the t-distribution.
Compute the confidence interval half-width for the given confidence level using the t-distribution.
Value parameters
- p_
-
the confidence level
Attributes
- Inherited from:
- Statistic
Compute the confidence interval half-width for the given confidence level using the z-distribution.
Compute the confidence interval half-width for the given confidence level using the z-distribution.
Value parameters
- p_
-
the confidence level
Attributes
- Inherited from:
- Statistic
Compute/estimate the mean absolue value (ma), e.g., Mean Absolute Error (MAE).
Compute/estimate the mean absolue value (ma), e.g., Mean Absolute Error (MAE).
Attributes
- Inherited from:
- Statistic
Return the maximum value in sample.
Compute/estimate the sample mean.
Return the minimum value in sample.
Compute/estimate the mean square (ms), e.g., Mean Square Error (MSE).
Compute/estimate the mean square (ms), e.g., Mean Square Error (MSE).
Attributes
- Inherited from:
- Statistic
Return the number of instances in sample as a double.
Return the number of instances in sample.
Compute/estimate the root mean square (rms), e.g., Root Mean Square Error (RMSE).
Compute/estimate the root mean square (rms), e.g., Root Mean Square Error (RMSE).
Attributes
- Inherited from:
- Statistic
Set accumulators.
Show the values of this collector's accumulators.
Return the summary statistics as a row/Array.
Compute/estimate the sample standard deviation.
Tally the next value and update accumulators.
Tally the next value and update accumulators.
Value parameters
- x
-
the value to tally (e.g., time in sytem)
Attributes
- Inherited from:
- Statistic
Tally the next vector of values and update accumulators.
Tally the next vector of values and update accumulators.
Value parameters
- v
-
the vector of values to tally (e.g., time in sytem)
Attributes
- Inherited from:
- Statistic
Compute/estimate the sample variance. The denominator is one less for unbiased (n-1) vs. maximum likelihood (n) estimators. Also use n for population variance.
Compute/estimate the sample variance. The denominator is one less for unbiased (n-1) vs. maximum likelihood (n) estimators. Also use n for population variance.
Attributes
- Inherited from:
- Statistic