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James–Stein estimator. The James–Stein estimator is a biased estimator of the mean, , of (possibly) correlated Gaussian distributed random variables with unknown means . It arose sequentially in two main published papers. The earlier version of the estimator was developed in 1956, [ 1] when Charles Stein reached a relatively shocking ...
Jungle Scout acquired "Forecastly", a predictive analytics and demand forecasting tool for Amazon sellers, in 2018. [2] In March 2021, Jungle Scout raised $110 million in growth capital led by Summit Partners, part of which was used to acquire Seattle-based Downstream Impact, a company specializing in Amazon advertising technology.
Background. An "estimator" or "point estimate" is a statistic (that is, a function of the data) that is used to infer the value of an unknown parameter in a statistical model. A common way of phrasing it is "the estimator is the method selected to obtain an estimate of an unknown parameter". The parameter being estimated is sometimes called the ...
A standard application of SURE is to choose a parametric form for an estimator, and then optimize the values of the parameters to minimize the risk estimate. This technique has been applied in several settings. For example, a variant of the James–Stein estimator can be derived by finding the optimal shrinkage estimator.
The Kaplan–Meier estimator, [1] [2] also known as the product limit estimator, is a non-parametric statistic used to estimate the survival function from lifetime data. In medical research, it is often used to measure the fraction of patients living for a certain amount of time after treatment. In other fields, Kaplan–Meier estimators may be ...
Inverse probability weighting is a statistical technique for estimating quantities related to a population other than the one from which the data was collected. Study designs with a disparate sampling population and population of target inference (target population) are common in application. [ 1] There may be prohibitive factors barring ...
In statistics, the bias of an estimator (or bias function) is the difference between this estimator 's expected value and the true value of the parameter being estimated. An estimator or decision rule with zero bias is called unbiased. In statistics, "bias" is an objective property of an estimator. Bias is a distinct concept from consistency ...
First use of an instrument variable occurred in a 1928 book by Philip G. Wright, best known for his excellent description of the production, transport and sale of vegetable and animal oils in the early 1900s in the United States, [6] [7] while in 1945, Olav Reiersøl applied the same approach in the context of errors-in-variables models in his dissertation, giving the method its name.