Bootstrapping To Estimate Standard Error
Contents |
WorkSocial MediaSoftwareProgrammingWeb Design & DevelopmentBusinessCareersComputers Online Courses B2B Solutions Shop for Books San Francisco, CA Brr, it´s cold outside Search Submit Learn more with dummies Enter your email to join our bootstrap to estimate standard error in r mailing list for FREE content right to your inbox. Easy! Your
Bootstrapping Standard Errors In Stata
email Submit RELATED ARTICLES The Bootstrap Method for Standard Errors and Confidence Intervals Key Concepts in Human
Calculate Standard Error Bootstrap
Biology and Physiology Chronic Pain and Individual Differences in Pain Perception Pain-Free and Hating It: Peripheral Neuropathy Neurotransmitters That Reduce or Block Pain Load more EducationScienceBiologyThe Bootstrap
Bootstrapping Standard Deviation
Method for Standard Errors and Confidence Intervals The Bootstrap Method for Standard Errors and Confidence Intervals Related Book Biostatistics For Dummies By John Pezzullo You can calculate the standard error (SE) and confidence interval (CI) of the more common sample statistics (means, proportions, event counts and rates, and regression coefficients). But an SE and how to estimate standard error of the mean CI exist (theoretically, at least) for any number you could possibly wring from your data -- medians, centiles, correlation coefficients, and other quantities that might involve complicated calculations, like the area under a concentration-versus-time curve (AUC) or the estimated five-year survival probability derived from a survival analysis. Formulas for the SE and CI around these numbers might not be available or might be hopelessly difficult to evaluate. Also, the formulas that do exist might apply only to normally distributed numbers, and you might not be sure what kind of distribution your data follows. Consider a very simple problem. Suppose you've measured the IQ of 20 subjects and have gotten the following results: 61, 88, 89, 89, 90, 92, 93, 94, 98, 98, 101, 102, 105, 108, 109, 113, 114, 115, 120, and 138. These numbers have a mean of 100.85 and a median of 99.5. Because you're a good scientist, you know that whenever you report some number you've calculated from y
whether or not the person got a speeding ticket. The data for women that received a ticket are shown below. estimate standard error from confidence interval Women, ticket:Sample: 103, 104, 109, 110, 120 Suppose we are interested in estimate standard error of proportion the following estimations: Estimate the population mean μ and get the standard deviation of the sample mean bootstrap values \(\bar{x}\). Estimate the population median η and get the standard deviation of the sample median. For (1), we have already found in the previous section that the sampling http://www.dummies.com/education/science/biology/the-bootstrap-method-for-standard-errors-and-confidence-intervals/ distribution of \(\bar{X}\) is approximately Normal (under certain conditions) with \[\begin{align}& \bar{x}=109.2\\& \text{SD}=6.76\\& n=5\\& \text{SD}(\bar{x})=\frac{s}{\sqrt{n}}=\frac{6.76}{\sqrt{5}}=3.023\end{align}\] What about the estimate of the population median, η. Let's denote the estimate M. We are interested in the standard deviation of the M. We can easily find the sample median by finding the middle observation of the ordered data. Thus, M = https://onlinecourses.science.psu.edu/stat464/node/80 109. But what about the standard deviation of the sample median? If we knew the underlying distribution of driving speeds of women that received a ticket, we could follow the method above and find the sampling distribution. To do this, we would follow these steps. Obtain a random sample of size n = 5 and calculate the sample median, M1. Gather another sample of size n = 5 and calculate M2. Repeat steps the steps until we obtained a desired number of sample medians, say 1000). Obtain the approximate distribution of the sample median and from there an estimate of the standard deviation. We can approximate the distribution by creating a histogram of all the sample medians. The trouble with this is that we do not know (nor want to assume) what distribution the data come from. A solution is to let the observed data represent the population and sample data from the original data. Therefore, we would sample n = observations from 103, 104, 109, 110, 120 with replac
Login Help Contact Us About Access You are not currently logged in. Access your personal account or get JSTOR access through your http://www.jstor.org/stable/2245500 library or other institution: login Log in to your personal account or through your institution. If You Use a Screen ReaderThis content is available through Read Online (Free) program, which relies on page scans. Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader. Statistical standard error Science Vol. 1, No. 1, Feb., 1986 Bootstrap Methods fo... Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical Accuracy B. Efron and R. Tibshirani Statistical Science Vol. 1, No. 1 (Feb., 1986), pp. 54-75 Published by: Institute of Mathematical Statistics Stable URL: http://www.jstor.org/stable/2245500 Page Count: 22 Read Online (Free) Subscribe ($19.50) Cite this Item Cite This Item estimate standard error Copy Citation Export Citation Export to RefWorks Export a RIS file (For EndNote, ProCite, Reference Manager, Zotero…) Export a Text file (For BibTex) Note: Always review your references and make any necessary corrections before using. Pay attention to names, capitalization, and dates. × Close Overlay Journal Info Statistical Science Description: The aim of Statistical Science is to present the full range of contemporary statistical thought at a technical level accessible to the broad community of practitioners, teachers, researchers, and students of statistics and probability. The journal publishes discussions of methodological and theoretical topics of current interest and importance, surveys of substantive research areas with promising statistical applications, comprehensive book reviews, discussions of classic articles from statistical literature, and interviews with distinguished statisticians and probabilists. Coverage: 1986-2012 (Vol. 1, No. 1 - Vol. 27, No. 4) Moving Wall Moving Wall: 3 years (What is the moving wall?) Moving Wall The "moving wall" represents the time period between the last issue available in JSTOR and the most recently published issue of a journal. Moving walls are generally represented in years. In rare ins