Robust statistics | Wikipedia audio article
Channel: wikipedia tts
Date: 2019-10-06

This is an audio version of the Wikipedia Article:
https://en.wikipedia.org/wiki/Robust_statistics


00:00:41 1 Introduction
00:01:22 2 Definition
00:02:04 3 Examples
00:02:45 4 Example: speed-of-light data
00:03:27 4.1 Estimation of location
00:04:08 4.2 Estimation of scale
00:04:50 4.3 Manual screening for outliers
00:05:31 4.4 Variety of applications
00:06:13 5 Measures of robustness
00:06:54 5.1 Breakdown point
00:07:56 5.1.1 Example: speed-of-light data
00:08:38 5.2 Empirical influence function
00:11:03 5.3 Influence function and sensitivity curve
00:12:26 5.4 Desirable properties
00:13:07 5.4.1 Rejection point
00:13:28 5.4.2 Gross-error sensitivity
00:13:49 5.4.3 Local-shift sensitivity
00:14:30 6 M-estimators
00:15:53 6.1 Properties of M-estimators
00:16:35 6.2 Influence function of an M-estimator
00:17:16 6.3 Choice of iψ/i and iρ/i
00:17:58 7 Robust parametric approaches
00:18:39 7.1 Example: speed-of-light data
00:19:21 8 Related concepts
00:20:02 9 Replacing outliers and missing values
00:20:44 10 See also



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SUMMARY
=======
Robust statistics are statistics with good performance for data drawn from a wide range of probability distributions, especially for distributions that are not normal.  Robust statistical methods have been developed for many common problems,  such as estimating location, scale, and regression parameters. One motivation is to produce statistical methods that are not unduly affected by outliers. Another motivation is to  provide methods with good performance when there are small departures from parametric distribution. For example, robust methods work well for mixtures of two normal distributions with different standard-deviations; under this model, non-robust methods like a t-test work poorly.