In generalized linear models. For small sample size,

                   In regression analysis, bootstrapping is an efficient tool for statistical
deduction, which focused on making a sampling distribution with the key idea of
resampling the originally observed data with replacement1. The term
bootstrapping, proposed by Bradley Efron in his “Bootstrap methods:
another look at the jackknife” published in 1979, is extracted from the cliché
of ‘pulling oneself up by one’s bootstraps’2. So, from the meaning
of this concept, sample data is considered as a population and replacement
samples are repeatedly drawn from the sample data, which is considered as a
population, to generate the statistical deduction about the sample data.  The essential bootstrap analogy states that “the
population is to the sample as the sample is to the bootstrap samples”2.

                   
The bootstrap falls into two types, parametric and nonparametric. Parametric
bootstrapping assumes that the original data set is drawn from some specific
distributions, e.g. normal distribution2. And the samples generally are
pulled as the same size as the original data set. Nonparametric
bootstrapping is right the one described in the start of this summary, which repeatedly
and randomly draws a certain size of bootstrapping samples from the original
data. Bootstrapping is quite useful in non-linear regression and generalized
linear models. For small sample size, the parametric bootstrapping method is highly
preferred. In large sample size, nonparametric bootstrapping method would be preferably
utilized. For a further clarification of nonparametric bootstrapping, a sample
data set, A = {x1, x2, …, xk} is randomly drawn from a population B = {X1,
X2, …, XK} and K is much larger than k. The statistic T = t(A) is considered as
an estimate of the corresponding population parameter P = t(B).2 Nonparametric
bootstrapping generates the estimate of the sampling distribution of a
statistic in an empirical way.  No
assumptions of the form of the population is necessary. Next, a sample of size k
is drawn from the elements of A with replacement, which represents as A?1 = {x?11, x?12, …, x?1k}. In the resampling,
a * note is added to distinguish resampled data from original data. Replacement
is mandatory and supposed to be repeated typically thousands of times, which is
still developing since computation power develops, otherwise only original
sample A would be generated.1 And for each bootstrap estimate of these samples, mean
is calculated to estimate the expectation of the bootstrapped statistics.  Mean minus T is the estimate of T’s bias. And
T?, the bootstrap variance estimate,
estimates the sampling variance of the
population, P. Then bootstrap confidence intervals can be constructed using
either bootstrap percentile interval approach or normal theory interval
approach. Confidence intervals by bootstrap percentile method is to use the empirical
quantiles of the bootstrap estimates, which is written as T?(lower)

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