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Detection Theory: Likelihood-Ratio Test, Statistical Hypothesis Testing, Minimax, Type I and Type Ii Errors, Receiver Operating Characteristic
Chapters: Likelihood-Ratio Test, Statistical Hypothesis Testing, Minimax, Type I and Type Ii Errors, Receiver Operating Characteristic, Location Estimation in Sensor Networks, Predictive Modeling, Neyman-pearson Lemma, Uniformly Most Powerful Test, Constant False Alarm Rate, Z-Factor, Change Detection, D', Statisticians' and Engineers' Cross-Reference of...
Chapters: Likelihood-Ratio Test, Statistical Hypothesis Testing, Minimax, Type I and Type Ii Errors, Receiver Operating Characteristic, Location Estimation in Sensor Networks, Predictive Modeling, Neyman-pearson Lemma, Uniformly Most Powerful Test, Constant False Alarm Rate, Z-Factor, Change Detection, D', Statisticians' and Engineers' Cross-Reference of Statistical Terms. Source: Wikipedia. Pages: 108. Not illustrated. Free updates online. Purchase includes a free trial membership in the publisher's book club where you can select from more than a million books without charge. Excerpt: In a statistical hypothesis test, there are two types of incorrect conclusions that can be drawn. The hypothesis can be inappropriately rejected (this is called type I error), or one can inappropriately retain the hypothesis (this is called type II error). The Greek letter is used to denote the probability of type I error, and the letter is used to denote the probability of type II error. Statisticians speak of two significant sorts of statistical error. The context, is that there is a "null hypothesis" which corresponds to a presumed default "state of nature", e.g., that an individual is free of disease, that an accused is innocent. Corresponding to the null hypothesis is an "alternative hypothesis" which corresponds to the opposite situation, that is, that the individual has the disease, that the accused is guilty. The goal is to determine accurately if the null hypothesis can be discarded in favor of the alternative. A test of some sort is conducted (a blood test, a legal trial), and data are obtained. The result of the test may be negative (that is, it does not indicate disease, guilt). On the other hand, it may be positive (that is, it may indicate disease, guilt). If the result of the test does not correspond with the actual state of nature, then an error has occurred, but if the result of the test corresponds with the actual state of n...More: http://booksllc.net/?id=5657877
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