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In psychology and cognitive science, a memory bias is a cognitive bias that either enhances or impairs the recall of a memory (either the chances that the memory will be recalled at all, or the amount of time it takes for it to be recalled, or both), or that alters the content of a reported memory. There are many types of memory bias, including:
In 1996, Elton, Gruber, and Blake showed that survivorship bias is larger in the small-fund sector than in large mutual funds (presumably because small funds have a high probability of folding). [8] They estimate the size of the bias across the U.S. mutual fund industry as 0.9% per annum, where the bias is defined and measured as:
AllSides Technologies Inc. is an American company that estimates the perceived political bias of content on online written news outlets. AllSides presents different versions of similar news stories from sources it rates as being on the political right, left, and center, with a mission to show readers news outside their filter bubble and expose media bias. [2]
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Thomas Asbridge's The First Crusade: A New History (2004) [11] is among the standard references used today. [12] [2] [13] [14] People's Crusade (1096) A prelude to the First Crusade led by Peter the Hermit, the first of what is known as the Popular Crusades. It is sometimes regarded as an integral part of the First Crusade, with the Princes ...
Loss aversion was also used to support the status quo bias in 1988, [9] and the equity premium puzzle in 1995. [10] In the 2000s, behavioural finance was an area with frequent application of this theory, [11] [12] including on asset prices and individual stock returns. [13] [14]
The scheme adjusts effectively for strength of schedule, in a way that is free of bias toward conference, tradition, or region." [ 2 ] Colley claims that his method is bias free for estimating the ranking of a team given a particular schedule. [ 2 ]
The bias (first term) is a monotone rising function of k, while the variance (second term) drops off as k is increased. In fact, under "reasonable assumptions" the bias of the first-nearest neighbor (1-NN) estimator vanishes entirely as the size of the training set approaches infinity. [12]