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Simulation Commands

  

The Statistics package provides optimized algorithms for simulating from all supported distributions as well as tools for creating custom random number generators, parametric and non-parametric bootstrap.

  

 

Bootstrap

compute bootstrap statistics

KernelDensitySample

sample a kernel density estimate

Sample

generate random sample

Examples

withStatistics:

Generate random sample drawn from the non-central beta distribution.

XRandomVariableNonCentralBeta3,10,2:

ASampleX,1000

Use the bootstrap to estimate the mean and the standard error of the mean.

BootstrapMean,X,replications=1000,output=value,standarderror

0.282229356166131,0.00399644288773034952

(1)

BootstrapMean,A,replications=1000,output=value,standarderror

0.275083017014237,0.00384426186874453797

(2)

Compare this with analytic results.

MeanX

1762148409+4790016000ⅇ−1

(3)

evalf30MeanX

0.28226746351970438745

(4)

MeanX,numeric

0.2822674635

(5)

Random sample involving two independent random variables.

YRandomVariableCauchy0,1

Y_R0

(6)

ZRandomVariableCauchy1,2

Z_R1

(7)

BSampleY2+Z2,105

See Also

Statistics

Statistics[Computation]