• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.4
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• pp.191-198
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• 2019

The purpose of this study is to explain the bootstrapping method to understand the process of overcoming the difficulties experienced by the limited resources of the initial start-up, through the example of Black Ruby Studio. Most nascent start-ups have constrained resources. This problem is a subject that is constantly being repeated in entrepreneurship research. Despite these problems, there are relatively few studies detailing the process of overcoming the initial difficulties of start-up. Bootstrapping is described as a way to reduce external inflows, reduce risk, and resolve funding issues internally. Many start-ups initially rely on bootstrapping as a way to solving scarce funds and limited resources. Therefore, this study reviewed the prior literatures in bootstrapping, and used 32 detailed item bootstrapping methods suggested in Winborg & Landstrom(2001) on bootstrapping in order to understand bootstrapping concept of start-up. This study gives insightful implication to prospective founders by using the bootstrapping method for survival of start-up and the process of overcoming the difficulties in the start-up.

• Proceedings of the KIEE Conference
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• 2003.11b
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• pp.179-182
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• 2003

A simulation on bootstrapping circuit has been performed by modelling a coaxial cable as a transmission line. It is shown that the bootstrapping circuit could be unstable due to the transmission line effect though an ideal amplifier is used. While the conventional bootstrapping circuit does not cancel the input capacitance of the input buffer, a new bootstrapping circuit that cancels input capacitance of the input buffer has been proposed. The proposed bootstrapping circuit consists of the input buffer of which gam is larger than 1 and a feedback resistor to control the loop gain. The proposed bootstrapping circuit has higher input impedance than that of the conventional circuit.

• Communications for Statistical Applications and Methods
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• v.22 no.3
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• pp.285-294
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• 2015

A permutation test is the popular and attractive alternative to derive asymptotic distributions of dimension test statistics in sufficient dimension reduction methodologies; however, recent studies show that a bootstrapping technique also can be used. We consider two types of bootstrapping dimension determination, which are partial and whole bootstrapping procedures. Numerical studies compare the permutation test and the two bootstrapping procedures; subsequently, real data application is presented. Considering two additional bootstrapping procedures to the existing permutation test, one has more supporting evidence for the dimension estimation of the central subspace that allow it to be determined more convincingly.

• Journal of the Korea Society for Simulation
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• v.24 no.4
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• pp.89-96
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• 2015

The mediation methods suggested by Baron and Kenny, Sobel, Aroian and Goodman, have widely used to test the mediating effect. However, as there are many problems in statistical test power, as well as statistical accuracy, a bootstrapping technique has been suggested as an alternative. In this paper, we adopt the phantom variables based on the bootstrapping technique to test the mediating effect in multiple mediator model consisting of three or more mediating variables. In particular, we formulate the multiple mediator model for analyzing the relations among organizational resources, the absorption capacity as mediating variables and technology commercialization capabilities. And using the bootstrapping approach, we analyzed the mediating effect of the absorption capacity by setting of phantom variables and calculated total indirect effect size and the statistical significance. The empirical results are as follows. First, we confirmed that the bootstrapping approach and the phantom variable is the very efficient and systematic mediation method. Second, we recognized that there is a difference in the mediating characteristics of the absorption capacity depending on the resource characteristics of human resources and material resources obviously.

• Communications for Statistical Applications and Methods
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• v.10 no.3
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• pp.1047-1056
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• 2003

In this paper, we consider a modified bootstrap scheme for inference of the GPH estimator and establish the sup-norm consistency of the proposed bootstrapping.

• Journal of Korean Institute of Industrial Engineers
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• v.26 no.1
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• pp.27-35
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• 2000

For a weighting method to be practically valid, it should produce weights which coincide with the relative importance of attributes perceived by the decision maker. In this paper, 'bootstrapping' is used to compare the practical validities of five weighting methods frequently used; the rank order centroid method, the rank reciprocal method, the rank sum method, the entropic method, and the geometric mean method. Bootstrapping refers to the procedure where the analysts allow the decision maker to make careful judgements on a series of similar cases, then infer statistically what weights he was implicitly using to arrive at the particular ranking. The weights produced by bootstrapping can therefore be regarded as well reflecting the decision maker's perceived relative importances. Bootstrapping and the five weighting methods were applied to a job selection problem. The results showed that both the rank order centroid method and the rank reciprocal method had higher level of practical validity than the other three methods, though a large difference could not be found either in the resulting weights or in the corresponding solutions.

• Korean Journal of Organic Agriculture
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• v.26 no.1
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• pp.33-41
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• 2018

The paper aims to estimate efficiency of watermelon by using a bootstrapping approach to generating efficiency estimates through Monte Carlo simulation resampling process. We use the input-output data for watermelon 107 farmers. The main results are as follows. The estimates of efficiency depends on the methodology. The estimates of general DEA is greater than the bootstrapping method. The technical efficiency and pure technical efficiency measure of watermelon is 0.72, 0.82 respectively. However the bias-corrected estimates are less than those of DEA. We know that the DEA estimator is an upward biased estimator. According to these results, the DEA bootstrapping model used here provides bias-corrected and confidence intervals for the point estimates, it is more preferable.

• Physical Therapy Korea
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• v.23 no.2
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• pp.93-99
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• 2016

Background: Parametric statistical procedures are typically conducted under the condition in which a sample distribution is statistically identical with its population. In reality, investigators use inferential statistics to estimate parameters based on the sample drawn because population distributions are unknown. The uncertainty of limited data from the sample such as lack of sample size may be a challenge in most rehabilitation studies. Objects: The purpose of this study is to review the bootstrapping method to overcome shortcomings of limited sample size in rehabilitation studies. Methods: Articles were reviewed. Results: Bootstrapping method is a statistical procedure that permits the iterative re-sampling with replacement from a sample when the population distribution is unknown. This statistical procedure is to enhance the representativeness of the population being studied and to determine estimates of the parameters when sample size are too limited to generalize the study outcome to target population. The bootstrapping method would overcome limitations such as type II error resulting from small sample sizes. An application on a typical data of a study represented how to deal with challenges of estimating a parameter from small sample size and enhance the uncertainty with optimal confidence intervals and levels. Conclusion: Bootstrapping method may be an effective statistical procedure reducing the standard error of population parameters under the condition requiring both acceptable confidence intervals and confidence level (i.e., p=.05).

• The Korean Journal of Applied Statistics
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• v.24 no.1
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• pp.207-216
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• 2011

Bootstrap is a resampling technique to find an estimate of parameters or to evaluate the estimate. This technique has been used in estimating parameters in linear model(LM) and generalized linear model(GLM). In this paper, we explore the possibility of applying Bootstrapping Residuals, Pairs, and an Estimating Equation that are most widely used in LM and GLM to the generalized estimating equation(GEE) algorithm for modelling repeatedly measured regression data sets. We compared three bootstrapping methods with coefficient and standard error estimates of GEE models from one simulated and one real data set. Overall, the estimates obtained from bootstrap methods are quite comparable, except that estimates from bootstrapping pairs are somewhat different from others. We conjecture that the strange behavior of estimates from bootstrapping pairs comes from the inconsistency of those estimates. However, we need a more thorough simulation study to generalize it since those results are coming from only two small data sets.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.42 no.3 s.303
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• pp.1-8
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• 2005

At IETF (Internet Engineering Task Force), recently RFC3775, RFC3776 documents about the mobile IPv6 were standardized by IETF (Internet Engineering Task Force). Those specifications propose that during the roaming, the mobile node sends securely the binding update to the home agent and the correspondent node after setting the security association between Mobile Node and Home Agent. But there is no secure bootstrapping method between a mobile node and a home agent at the two RFC documents. This paper proposed a method for the secure bootstrapping between a mobile node and a home agent. This makes the authentication, binding update, home agent assignment, security association distribution through the AAA-based secure channel between mobile node and home agent. And the proposed method was analyzed in the view of the procedure, round trip and security strength.