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Quantile regression is a type of regression analysis used in statistics and econometrics. Whereas the method of least squares estimates the conditional mean of the response variable across values of the predictor variables, quantile regression estimates the conditional median (or other quantiles) of the response variable.
In robust statistics, repeated median regression, also known as the repeated median estimator, is a robust linear regression algorithm. The estimator has a breakdown point of 50%. [ 1 ] Although it is equivariant under scaling, or under linear transformations of either its explanatory variable or its response variable, it is not under affine ...
An example of the first resample might look like this X 1 * = x 2, x 1, x 10, x 10, x 3, x 4, x 6, x 7, x 1, x 9. There are some duplicates since a bootstrap resample comes from sampling with replacement from the data. Also the number of data points in a bootstrap resample is equal to the number of data points in our original observations.
Cuff-Less Blood Pressure Estimation Dataset Cleaned vital signals from human patients which can be used to estimate blood pressure. 125 Hz vital signs have been cleaned. 12,000 Text Classification, regression 2015 [156] [157] M. Kachuee et al. Gas Sensor Array Drift Dataset
In statistics, the Hodges–Lehmann estimator is a robust and nonparametric estimator of a population's location parameter.For populations that are symmetric about one median, such as the Gaussian or normal distribution or the Student t-distribution, the Hodges–Lehmann estimator is a consistent and median-unbiased estimate of the population median.
Analogously to how the median generalizes to the geometric median (GM) in multivariate data, MAD can be generalized to the median of distances to GM (MADGM) in n dimensions. This is done by replacing the absolute differences in one dimension by Euclidean distances of the data points to the geometric median in n dimensions. [5]
For example, the ML estimator from the previous example may be attained as the limit of Bayes estimators with respect to a uniform prior, [,] with increasing support and also with respect to a zero-mean normal prior (,) with increasing variance. So neither the resulting ML estimator is unique minimax nor the least favorable prior is unique.
In statistics, the method of estimating equations is a way of specifying how the parameters of a statistical model should be estimated.This can be thought of as a generalisation of many classical methods—the method of moments, least squares, and maximum likelihood—as well as some recent methods like M-estimators.