Bootstrapping

(verb)

Financial bootstrapping is a term used to cover different methods when someone wants to avoid using the financial resources of external investors. The use of private credit card debt is the most known form of bootstrapping, but a wide variety of methods are available for entrepreneurs. While bootstrapping involves a risk for the founders, the absence of any other stakeholder gives the founders more freedom to develop the company.

Examples of Bootstrapping in the following topics:

  • Financing Company Operations

    • Many successful companies including Dell Computers and Facebook were founded using financial bootstrapping.
    • Financial bootstrapping is a term used to cover different methods for avoiding the use of financial resources that come from external investors.
    • The use of private credit card debt is the most known form of bootstrapping, but a wide variety of methods are available for entrepreneurs.
    • While bootstrapping involves a risk for the founders, the absence of any other stakeholder gives the founders more freedom to develop the company.
    • Many successful companies, including Dell Computers and Facebook, were founded using financial bootstrapping.

  • Single-Population Inferences

    • Bootstrapping is a method for assigning measures of accuracy to sample estimates.
    • Bootstrapping may also be used for constructing hypothesis tests.
    • The simplest bootstrap method involves taking the original data set of $N$ heights, and, using a computer, sampling from it to form a new sample (called a 'resample' or bootstrap sample) that is also of size $N$.
    • We now have a histogram of bootstrap means.
    • A great advantage of bootstrap is its simplicity.

  • Hypotheses about two paired means or densities

    • Network>Compare densities>Paired (same node) compares the densities of two relations for the same actors, and calculates estimated standard errors to test differences by bootstrap methods.
    • Results for both the standard approach and the bootstrap approach (this time, we ran 10,000 sub-samples) are reported in the output.
    • The standard error of the difference by the classical method is .0697; the standard error by bootstrap estimate is .1237.
    • By the bootstrap method, we can see that there is a two-tailed probability of .0178.

  • Hypotheses about one mean or density

    • The parameter "Number of samples" is used for estimating the standard error for the test by the means of "bootstrapping" or computing estimated sampling variance of the mean by drawing 5000 random sub-samples from our network, and constructing a sampling distribution of density measures.
    • However, if we use the bootstrap method of constructing 5000 networks by sampling random sub-sets of nodes each time, and computing the density each time, the mean of this sampling distribution turns out to be .4893, and its standard deviation (or the standard error) turns out to be .1201.
    • The classical formula gives an estimate of the standard error (.0528) that is much smaller than than that created by the bootstrap method (.1201).

  • References

    • Tape-assisted reciprocal teaching: Cognitive bootstrapping for poor decoders.

  • Spot Rates, Forward Rates, and Cross Rates

    • For bonds, spot rates are estimated via the bootstrapping method, which uses prices of the securities currently trading in market, that is, from the cash or coupon curve.

  • Distribution-Free Tests

  • Interpreting a Confidence Interval

    • Bootstrapping - In situations where the distributional assumptions for the above methods are uncertain or violated, resampling methods allow construction of confidence intervals or prediction intervals.

  • Introduction to Starting From What You Have

    • Think of it as the first step in a bootstrapping process, to bring the project to a kind of minimum activation energy.