# Bootstrap

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## Bootstrap

Term used to describe the start-up of a company with very little capital.

## Bootstrap

1. To start a company with personal finances rather than through loans or venture capital. This is obviously a large risk to the entrepreneur as he/she has no recourse should the business fail. On the other hand, it allows the entrepreneur to maintain control of the business and has the potential to be very successful. It is famously said that Ross Perot established Electronic Data Systems with \$1,000 in personal savings; he maintained complete control of the company until its IPO six years later. This is an example of bootstrapping. See also: Seed money.

2. To calculate the yield curve on a zero-coupon Treasury bill. Because the U.S. Treasury does not issue new T-bills constantly, bootstrapping is used to create a yield curve by filling in the missing yields on the T-bills.

## bootstrap

To assist a new business in getting off the ground.
References in periodicals archive ?
The Use of Bootstrap Financing Among Small Technology-Based Firms, Journal of Developmental Entrepreneurship, 9(2), 145-159.
We used STATA's ""scfboot" command with 999 bootstrap replications.
To apply the bootstrap procedure, it is assumed that the original sample data is produced by the DGP and that we can simulate the DGP by using the 'new' or pseudo data set that is derived from the actual data set.
This makes for an ideal situation where the bootstrap approach can be applied.
The usual methods, known in the literature as parametric and nonparametric bootstrap (DAVISON; HINKLEY, 1997), may provide unreliable estimates since the probability of a sample unit be randomly selected to compose a bootstrap sample follows a uniform discrete distribution.
Under each scenario, 1000 data sets were generated using the power method proposed by Fleishman (1978), and the properties of the estimators were compared for bootstrap and REML procedures.
Calculate the maximum, minimum and difference of the bootstrap CUSUM, denoted [S.
Algunos autores estudian las desventajas del bootstrap no parametrico, por ejemplo Andrews (2000) comenta que en modelos con coeficientes aleatorios, a menudo las varianzas estimadas de algunos de los coeficientes aleatorios son pequenas y por lo tanto las varianzas verdaderas de algunos de estos coeficientes podrian ser cero.
Those who attack this form of bootstrapping contend that the relevant bootstrap is illegitimate because it goes too far.
You can find out more about Bootstrap 2 here or check out its source on GitHub.
The combined use of Bootstrap methods and Artificial Neural Networks (ANNs) has also been used in Forecasting in the past (Focarelli 2005).
As result replicability is essential to science and difficult to achieve through external replicability, the present paper notes the insufficiency of null hypothesis statistical significance testing (NHSST) and explains the bootstrap as a plausible alternative, with a heuristic example to illustrate the bootstrap method.

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