In statistics, an estimator is a rule for calculating an estimate of a given quantity based on observed data: thus the rule (the estimator), the quantity of interest (the estimand) and its result (the estimate) are distinguished. For example, the sample mean is a commonly used estimator of the population mean. There are point and interval estimators. The point estimators yield single-valued results. This is in contrast to an interval estimator, where the result would be a range of plausible values. "Single value" does not necessarily mean "single number", but includes vector valued or function valued estimators. Estimation theory is concerned with the properties of estimators; that is, with defining properties that can be used to compare different estimators (different rules for creating estimates) for the same quantity, based on the same data. Such properties can be used to determine the best rules to use under given circumstances. However, in robust statistics, statistical theory goes on to consider the balance between having good properties, if tightly defined assumptions hold, and having less good properties that hold under wider conditions. (Wikipedia).
Definition of an estimator. Examples of estimators. Definition of an unbiased estimator.
From playlist Machine Learning
(ML 17.3) Monte Carlo approximation
From playlist Machine Learning
Introduction to Estimation Theory
http://AllSignalProcessing.com for more great signal-processing content: ad-free videos, concept/screenshot files, quizzes, MATLAB and data files. General notion of estimating a parameter and measures of estimation quality including bias, variance, and mean-squared error.
From playlist Estimation and Detection Theory
Maximum Likelihood Estimation Examples
http://AllSignalProcessing.com for more great signal processing content, including concept/screenshot files, quizzes, MATLAB and data files. Three examples of applying the maximum likelihood criterion to find an estimator: 1) Mean and variance of an iid Gaussian, 2) Linear signal model in
From playlist Estimation and Detection Theory
This video explains how to determine mean, median and mode. It also provided examples. http://mathispower4u.yolasite.com/
From playlist Statistics: Describing Data
More Standard Deviation and Variance
Further explanations and examples of standard deviation and variance
From playlist Unit 1: Descriptive Statistics
EstimatingRegressionCoefficients.1.EstimatingResidualVariance
This video is brought to you by the Quantitative Analysis Institute at Wellesley College. The material is best viewed as part of the online resources that organize the content and include questions for checking understanding: https://www.wellesley.edu/qai/onlineresources
From playlist Estimating Regression Coefficients
Statistics 5_1 Confidence Intervals
In this lecture explain the meaning of a confidence interval and look at the equation to calculate it.
From playlist Medical Statistics
Determining values of a variable at a particular percentile in a normal distribution
From playlist Unit 2: Normal Distributions
Ruby On Ales 2015 - Estimation Blackjack and Other Games: a Comedic Compendium
By, Amy Unger Running a good estimation meeting is hard. It’s easy to get lost in the weeds of implementation, and let weird social interactions slip into our estimating process. You, too, may have played Estimation Blackjack without realizing it, being “out” if you give an estimate higher
From playlist Ruby on Ales 2015
23. Classical Statistical Inference I
MIT 6.041 Probabilistic Systems Analysis and Applied Probability, Fall 2010 View the complete course: http://ocw.mit.edu/6-041F10 Instructor: John Tsitsiklis License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT 6.041SC Probabilistic Systems Analysis and Applied Probability, Fall 2013
Power Analysis, Clearly Explained!!!
If you're doing an experiment, a Power Analysis is a must. It ensures reproducibility by helping you avoid p-hacking and being fooled by false positives. NOTE: This StatQuest assumes that you are already familiar with the concept of Statistical Power, Population Parameters vs Estimated Pa
From playlist StatQuest
Ensemble Methods in Scikit Learn
We explore the really heavy hitters: ensemble methods. We go over the meta estimators: voting classifier, adaboost, and bagging. And then we dive into the two power houses: random forests and gradient boosting. Associated Github Commit: https://github.com/knathanieltucker/bit-of-data-scie
From playlist A Bit of Data Science and Scikit Learn
The Optimizer's Curse: Disappointing Decisions
My entry in 3blue1brown's Summer of Math Exposition 2, enjoy :) (P.S. you can download a copy of my blackboard here if you're interested: https://www.dropbox.com/s/y18qjo4klz70jpm/recordingfinal.png?dl=0). One mistake that I made was not being more specific about the circumstances in whi
From playlist Summer of Math Exposition 2 videos
Nexus Trimester - Gábor Lugosi (Pompeu Fabra University) 1/2
How to estimate the mean of a random variable? - Part 1 Gábor Lugosi (Pompeu Fabra University) March 14, 2016 Abstract: Given n independent, identically distributed copies of a random variable, one is interested in estimating the expected value. Perhaps surprisingly, there are still open
From playlist 2016-T1 - Nexus of Information and Computation Theory - CEB Trimester
RubyConf 2015 - I Estimate this Talk will be 20 Minutes Long, Give or Take 10 Minutes
I Estimate this Talk will be 20 Minutes Long, Give or Take 10 Minutes by Noel Rappin Estimates are like weather forecasts. Getting them right is hard, and everybody complains when you are wrong. Estimating projects is often critically important to the people who pay us to develop software
From playlist RubyConf 2015
22. Bayesian Statistical Inference II
MIT 6.041 Probabilistic Systems Analysis and Applied Probability, Fall 2010 View the complete course: http://ocw.mit.edu/6-041F10 Instructor: John Tsitsiklis License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT 6.041SC Probabilistic Systems Analysis and Applied Probability, Fall 2013
Special Topics - The Kalman Filter (6 of 55) A Simple Example of the Kalman Filter (Continued)
Visit http://ilectureonline.com for more math and science lectures! In this video I will use the Kalman filter to zero in the true temperature given a sample of 4 measurements. Next video in this series can be seen at: https://youtu.be/-cD7WkbAIL0
From playlist SPECIAL TOPICS 1 - THE KALMAN FILTER