Computational statistics | Resampling (statistics)
Bootstrapping is any test or metric that uses random sampling with replacement (e.g. mimicking the sampling process), and falls under the broader class of resampling methods. Bootstrapping assigns measures of accuracy (bias, variance, confidence intervals, prediction error, etc.) to sample estimates. This technique allows estimation of the sampling distribution of almost any statistic using random sampling methods. Bootstrapping estimates the properties of an estimand (such as its variance) by measuring those properties when sampling from an approximating distribution. One standard choice for an approximating distribution is the empirical distribution function of the observed data. In the case where a set of observations can be assumed to be from an independent and identically distributed population, this can be implemented by constructing a number of resamples with replacement, of the observed data set (and of equal size to the observed data set). It may also be used for constructing hypothesis tests. It is often used as an alternative to statistical inference based on the assumption of a parametric model when that assumption is in doubt, or where parametric inference is impossible or requires complicated formulas for the calculation of standard errors. (Wikipedia).
Bootstrap is a set of templates and tools that fast-track front-end web development. Basically, it makes your site look pretty and professional! We'll go over tips and tricks and make sure you're taking full advantage of the opportunities Bootstrap can provide.
From playlist CS50 Seminars 2016
Code: bootstrapping confidence intervals
This video is part of a full course on statistics and machine-learning. The full course includes 35 hours of video instruction, tons of Python and MATLAB code, and access to the Q&A forum. More information available here: https://www.udemy.com/course/statsml_x/?couponCode=202006 For a co
From playlist Statistics and machine learning
Bootstrap Calibration - Statistical Inference
In this video I introduce you to bootstrap calibration, a technique for improving your confidence intervals, and explain how and why it is so useful.
From playlist Statistical Inference
Bootstrapping is one of the simplest, yet most powerful methods in all of statistics. It provides us an easy way to get a sense of what might happen if we could repeat an experiment a bunch of times. It turns point estimates into distributions that can be used to calculate all kinds of stu
From playlist StatQuest
(ML 2.6) Bootstrap aggregation (Bagging)
The statistical technique of "bagging", to reduce the variance of a classification or regression procedure. A playlist of these Machine Learning videos is available here: http://www.youtube.com/my_playlists?p=D0F06AA0D2E8FFBA
From playlist Machine Learning
Coefficient Of Variation - Statistical Inference
In this video I talk about the coefficient of variation wherein I explain how big 'N' should be -- how many bootstrap samples should we take? This is one of this series' most technical topics but bear with me -- it covers super useful concepts!
From playlist Statistical Inference
Lecture on the bootstrap method to assess uncertainty in a sample statistic from the sample itself.
From playlist Data Analytics and Geostatistics
Using Bootstrapping to Calculate p-values!!!
Bootstrapping give us an easy way to calculate p-values for just about anything - no fancy math required! In this StatQuest, we walk through the process of calculating the p-value for a mean, and then a median, step-by-step. DOUBLE BAM! NOTE: This StatQuest assumes that you are already fa
From playlist StatQuest
Data Science Basics: Bootstrap
Live Jupyter walk-through of bootstrap for uncertainty modeling in Python. I demonstrate that we can bootstrap to calculate uncertainty, due to data paucity, for any statistic! This should be enough to get anyone started building data analytics workflows in Python. The demonstrated workfl
From playlist Data Science Basics in Python
0:15 - Review 2:29 - Learning objectives 2:48 - 1. Construct and interpret sampling distributions using StatKey 3:36 - StatKey 10:42 - Review of terms 12:12 - 2. Explain the general form of a confidence interval 16:59 - 3. Interpret a confidence interval 23:47 - 4. Explain the
From playlist STAT 200 Video Lectures
Random matrices and high-dimensional stats: Beyond covariance matrices – N. El Karoui – ICM2018
Probability and Statistics Invited Lecture 12.11 Random matrices and high-dimensional statistics: Beyond covariance matrices Noureddine El Karoui Abstract: The last twenty-or-so years have seen spectacular progress in our understanding of the fine spectral properties of large-dimensional
From playlist Probability and Statistics
Bootstrap and Monte Carlo Teacher: Dr. Michael Pyrcz For more webinars & events please checkout: http://daytum.io/events Website: https://www.daytum.io/ Twitter: https://twitter.com/daytum_io?lang=en LinkedIn: https://www.linkedin.com/company/35593451 Data Science Education for Energy P
From playlist daytum Free Webinar Series
Statistical Learning: 5.R.2 Bootstrap
Statistical Learning, featuring Deep Learning, Survival Analysis and Multiple Testing You are able to take Statistical Learning as an online course on EdX, and you are able to choose a verified path and get a certificate for its completion: https://www.edx.org/course/statistical-learning
From playlist Statistical Learning
Saharon Rosset: "Bootstrap - The Statistician's Magic Wand"
Computational Genomics Summer Institute 2016 "Bootstrap - The Statistician's Magic Wand" Saharon Rosset, Tel Aviv University Institute for Pure and Applied Mathematics, UCLA July 21, 2016 For more information: http://computationalgenomics.bioinformatics.ucla.edu/
From playlist Computational Genomics Summer Institute 2016
Statistical Learning: 5.5 More on the Bootstrap
Statistical Learning, featuring Deep Learning, Survival Analysis and Multiple Testing You are able to take Statistical Learning as an online course on EdX, and you are able to choose a verified path and get a certificate for its completion: https://www.edx.org/course/statistical-learning
From playlist Statistical Learning
Bootstrapping and confidence intervals in t-test | SPSS
In this video, I will demonstrate how to do bootstrapping and interpret confidence intervals. I also discuss the relationship between t values, mean differences, and standard error of mean. I recommend reading this paper for more information: https://www.frontiersin.org/articles/10.3389/f
From playlist Independent Samples t-Test
Lect.10D: Bootstrap Confidence Intervals, One-Sample, Including Example Lecture 10
Lecture with Per B. Brockhoff. Lecture 10. Chapters: 00:00 - Introduction; 01:45 - Confidence Intervals Using Simulation: Bootstrapping; 06:00 - Non-Parametric Bootstrap For The One-Sample Situation; 09:00 - Example 2, One-Sample; 11:15 - Example 2, Solution In R;
From playlist DTU: Introduction to Statistics | CosmoLearning.org