Jackknife statistics help
Jackknife resampling - Wikipedia
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Statistics Roundtable: The Trusty Jackknife
MathWorks does not warrant, and disclaims all liability for, the accuracy, suitability, or fitness for purpose of the translation. Each of the n rows of jackstat contains the results of applying jackfun to one jackknife sample. Row i of jackstat contains the results for the sample consisting of X with the jackknife statistics th row help If X is a row vector, it is converted to a column vector. They may be help, column vectors, or matrices.
Scalar data are passed to jackknife statistics help unchanged.
Jackknife statistics help arguments must have the same number jackknife statistics help rows, and each jackknife sample omits the same row from these arguments. Set 'Options' as a structure you create with statset. If trueuse multiple processors to compute jackknife iterations. If the Parallel Computing Toolbox is not installed, jackknife statistics help computation occurs in serial mode.
The Trusty Jackknife
Default is jackknife statistics helpmeaning serial computation. Estimate the help primary homework the MLE variance estimator of help samples taken from the vector y using jackknife. The bias has a known formula in jackknife statistics problem, so you can compare the jackknife value to this formula. To run in parallel, set the 'UseParallel' option to true.
Set the 'UseParallel' field of the options source to true using statset and specify the jackknife statistics help name-value pair argument in the call to this function.
Resampling (statistics) - Wikipedia
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Resampling (statistics)
This is machine translation Translated by. See Also bootstrp histogram ksdensity random randsample Topics Jackknife Resampling.
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Outliers are a continual source of problems when analyzing data. A few questionable data points can skew your distribution, make significant results seem insignificant and generally ruin your day. It is called the jackknife.
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In statistics , resampling is any of a variety of methods for doing one of the following:. Bootstrapping is a statistical method for estimating the sampling distribution of an estimator by sampling with replacement from the original sample, most often with the purpose of deriving robust estimates of standard errors and confidence intervals of a population parameter like a mean , median , proportion , odds ratio , correlation coefficient or regression coefficient.
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