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Subsampling is an alternative method for approximating the sampling distribution of an estimator. The two key differences to the bootstrap are: the resample size is smaller than the sample size and; resampling is done without replacement. The advantage of subsampling is that it is valid under much weaker conditions compared to the bootstrap.
The simplest bootstrap method involves taking the original data set of heights, and, using a computer, sampling from it to form a new sample (called a 'resample' or bootstrap sample) that is also of size N. The bootstrap sample is taken from the original by using sampling with replacement (e.g. we might 'resample' 5 times from [1,2,3,4,5] and ...
v. t. e. Bootstrap aggregating, also called bagging (from b ootstrap agg regat ing ), is a machine learning ensemble meta-algorithm designed to improve the stability and accuracy of machine learning algorithms used in statistical classification and regression. It also reduces variance and helps to avoid overfitting.
In statistics, the jackknife (jackknife cross-validation) is a cross-validation technique and, therefore, a form of resampling . It is especially useful for bias and variance estimation. The jackknife pre-dates other common resampling methods such as the bootstrap. Given a sample of size , a jackknife estimator can be built by aggregating the ...
Sample size determination or estimation is the act of choosing the number of observations or replicates to include in a statistical sample. The sample size is an important feature of any empirical study in which the goal is to make inferences about a population from a sample. In practice, the sample size used in a study is usually determined ...
For correlated random variables the sample variance needs to be computed according to the Markov chain central limit theorem. Independent and identically distributed random variables with random sample size. There are cases when a sample is taken without knowing, in advance, how many observations will be acceptable according to some criterion.
Set up two statistical hypotheses, H1 and H2, and decide about α, β, and sample size before the experiment, based on subjective cost-benefit considerations. These define a rejection region for each hypothesis. 2 Report the exact level of significance (e.g. p = 0.051 or p = 0.049).
Where, ¯ is the overall sample mean, ¯ is the group j sample mean, k is the number of groups and n j is sample size of group j. The F statistic is distributed F (k-1,n-k),(α) under assumption of null hypothesis and normality assumption. F test is considered robust in some situations, even when the normality assumption isn't met.