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The best example of the plug-in principle, the bootstrapping method. 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 ...
Bootstrap model. The term " bootstrap model " is used for a class of theories that use very general consistency criteria to determine the form of a quantum theory from some assumptions on the spectrum of particles. It is a form of S-matrix theory .
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Plus–minus sign. The plus–minus sign, ±, is a symbol with multiple meanings: In mathematics, it generally indicates a choice of exactly two possible values, one of which is obtained through addition and the other through subtraction. In statistics and experimental sciences, the sign commonly indicates the confidence interval or uncertainty ...
The statistic of the bootstrap needs to accept an interval of the time series and return the summary statistic on it. The call to the stationary bootstrap needs to specify an appropriate mean interval length. Applications. Cross-validation can be used to compare the performances of different predictive modeling procedures.
Pivotal quantity. In statistics, a pivotal quantity or pivot is a function of observations and unobservable parameters such that the function's probability distribution does not depend on the unknown parameters (including nuisance parameters ). [1] A pivot need not be a statistic — the function and its 'value' can depend on the parameters of ...
The plus sign ( +) and the minus sign ( −) are mathematical symbols used to denote positive and negative functions, respectively. In addition, + represents the operation of addition, which results in a sum, while − represents subtraction, resulting in a difference. [1] Their use has been extended to many other meanings, more or less analogous.
Since the sample does not include all members of the population, statistics of the sample (often known as estimators), such as means and quartiles, generally differ from the statistics of the entire population (known as parameters).