Sensitivity Index
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Sensitivity Index
The sensitivity index or discriminability index or detectability index is a dimensionless statistic used in signal detection theory. A higher index indicates that the signal can be more readily detected. Definition The discriminability index is the separation between the means of two distributions (typically the signal and the noise distributions), in units of the standard deviation. Equal variances/covariances For two univariate distributions a and b with the same standard deviation, it is denoted by d' ('dee-prime'): : d' = \frac. In higher dimensions, i.e. with two multivariate distributions with the same variance-covariance matrix \mathbf, (whose symmetric square-root, the standard deviation matrix, is \mathbf), this generalizes to the Mahalanobis distance between the two distributions: : d'=\sqrt = \lVert \mathbf^(\boldsymbol_a-\boldsymbol_b) \rVert = \lVert \boldsymbol_a-\boldsymbol_b \rVert /\sigma_, where \sigma_ = 1/ \lVert\mathbf^\boldsymbol\rVert is the 1d slice of the ...
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Discriminability Index
The sensitivity index or discriminability index or detectability index is a dimensionless statistic used in signal detection theory. A higher index indicates that the signal can be more readily detected. Definition The discriminability index is the separation between the means of two distributions (typically the signal and the noise distributions), in units of the standard deviation. Equal variances/covariances For two univariate distributions a and b with the same standard deviation, it is denoted by d' ('dee-prime'): : d' = \frac. In higher dimensions, i.e. with two multivariate distributions with the same variance-covariance matrix \mathbf, (whose symmetric square-root, the standard deviation matrix, is \mathbf), this generalizes to the Mahalanobis distance between the two distributions: : d'=\sqrt = \lVert \mathbf^(\boldsymbol_a-\boldsymbol_b) \rVert = \lVert \boldsymbol_a-\boldsymbol_b \rVert /\sigma_, where \sigma_ = 1/ \lVert\mathbf^\boldsymbol\rVert is the 1d slice of the ...
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Statistic
A statistic (singular) or sample statistic is any quantity computed from values in a sample which is considered for a statistical purpose. Statistical purposes include estimating a population parameter, describing a sample, or evaluating a hypothesis. The average (or mean) of sample values is a statistic. The term statistic is used both for the function and for the value of the function on a given sample. When a statistic is being used for a specific purpose, it may be referred to by a name indicating its purpose. When a statistic is used for estimating a population parameter, the statistic is called an ''estimator''. A population parameter is any characteristic of a population under study, but when it is not feasible to directly measure the value of a population parameter, statistical methods are used to infer the likely value of the parameter on the basis of a statistic computed from a sample taken from the population. For example, the sample mean is an unbiased estimator of ...
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Signal Detection Theory
Detection theory or signal detection theory is a means to measure the ability to differentiate between information-bearing patterns (called stimulus in living organisms, signal in machines) and random patterns that distract from the information (called noise, consisting of background stimuli and random activity of the detection machine and of the nervous system of the operator). In the field of electronics, signal recovery is the separation of such patterns from a disguising background. According to the theory, there are a number of determiners of how a detecting system will detect a signal, and where its threshold levels will be. The theory can explain how changing the threshold will affect the ability to discern, often exposing how adapted the system is to the task, purpose or goal at which it is aimed. When the detecting system is a human being, characteristics such as experience, expectations, physiological state (e.g., fatigue) and other factors can affect the threshold app ...
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Mahalanobis Distance
The Mahalanobis distance is a measure of the distance between a point ''P'' and a distribution ''D'', introduced by P. C. Mahalanobis in 1936. Mahalanobis's definition was prompted by the problem of identifying the similarities of skulls based on measurements in 1927. It is a multi-dimensional generalization of the idea of measuring how many standard deviations away ''P'' is from the mean of ''D''. This distance is zero for ''P'' at the mean of ''D'' and grows as ''P'' moves away from the mean along each principal component axis. If each of these axes is re-scaled to have unit variance, then the Mahalanobis distance corresponds to standard Euclidean distance in the transformed space. The Mahalanobis distance is thus unitless, scale-invariant, and takes into account the correlations of the data set. Definition Given a probability distribution Q on \R^N, with mean \vec = (\mu_1, \mu_2, \mu_3, \dots , \mu_N)^\mathsf and positive-definite covariance matrix S, the Mahalanobis dis ...
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Non-central Chi-squared Distribution
In probability theory and statistics, the noncentral chi-squared distribution (or noncentral chi-square distribution, noncentral \chi^2 distribution) is a noncentral generalization of the chi-squared distribution. It often arises in the power analysis of statistical tests in which the null distribution is (perhaps asymptotically) a chi-squared distribution; important examples of such tests are the likelihood-ratio tests. Definitions Background Let (X_1,X_2, \ldots, X_i, \ldots,X_k) be ''k'' independent, normally distributed random variables with means \mu_i and unit variances. Then the random variable : \sum_^k X_i^2 is distributed according to the noncentral chi-squared distribution. It has two parameters: k which specifies the number of degrees of freedom (i.e. the number of X_i), and \lambda which is related to the mean of the random variables X_i by: : \lambda=\sum_^k \mu_i^2. \lambda is sometimes called the noncentrality parameter. Note that some references define \ ...
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Receiver Operating Characteristic
A receiver operating characteristic curve, or ROC curve, is a graphical plot that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied. The method was originally developed for operators of military radar receivers starting in 1941, which led to its name. The ROC curve is created by plotting the true positive rate (TPR) against the false positive rate (FPR) at various threshold settings. The true-positive rate is also known as sensitivity, recall or ''probability of detection''. The false-positive rate is also known as ''probability of false alarm'' and can be calculated as (1 − specificity). The ROC can also be thought of as a plot of the power as a function of the Type I Error of the decision rule (when the performance is calculated from just a sample of the population, it can be thought of as estimators of these quantities). The ROC curve is thus the sensitivity or recall as a function of fall-out. In general, if the ...
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Generalized Chi-squared Distribution
In probability theory and statistics, the generalized chi-squared distribution (or generalized chi-square distribution) is the distribution of a quadratic form of a multinormal variable (normal vector), or a linear combination of different normal variables and squares of normal variables. Equivalently, it is also a linear sum of independent noncentral chi-square variables and a normal variable. There are several other such generalizations for which the same term is sometimes used; some of them are special cases of the family discussed here, for example the gamma distribution. Definition The generalized chi-squared variable may be described in multiple ways. One is to write it as a linear sum of independent noncentral chi-square variables and a normal variable:Davies, R.B. (1973) Numerical inversion of a characteristic function. Biometrika, 60 (2), 415–417Davies, R,B. (1980) "Algorithm AS155: The distribution of a linear combination of ''χ''2 random variables", ''A ...
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Receiver Operating Characteristic
A receiver operating characteristic curve, or ROC curve, is a graphical plot that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied. The method was originally developed for operators of military radar receivers starting in 1941, which led to its name. The ROC curve is created by plotting the true positive rate (TPR) against the false positive rate (FPR) at various threshold settings. The true-positive rate is also known as sensitivity, recall or ''probability of detection''. The false-positive rate is also known as ''probability of false alarm'' and can be calculated as (1 − specificity). The ROC can also be thought of as a plot of the power as a function of the Type I Error of the decision rule (when the performance is calculated from just a sample of the population, it can be thought of as estimators of these quantities). The ROC curve is thus the sensitivity or recall as a function of fall-out. In general, if the ...
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Summary Statistics
In descriptive statistics, summary statistics are used to summarize a set of observations, in order to communicate the largest amount of information as simply as possible. Statisticians commonly try to describe the observations in * a measure of location, or central tendency, such as the arithmetic mean * a measure of statistical dispersion like the standard deviation, standard mean absolute deviation * a measure of the shape of the distribution like skewness or kurtosis * if more than one variable is measured, a measure of correlation and dependence, statistical dependence such as a Pearson product-moment correlation coefficient, correlation coefficient A common collection of order statistics used as summary statistics are the five-number summary, sometimes extended to a seven-number summary, and the associated box plot. Entries in an analysis of variance table can also be regarded as summary statistics. Examples Location Common measures of location, or central tendency, are ...
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Effect Size
In statistics, an effect size is a value measuring the strength of the relationship between two variables in a population, or a sample-based estimate of that quantity. It can refer to the value of a statistic calculated from a sample of data, the value of a parameter for a hypothetical population, or to the equation that operationalizes how statistics or parameters lead to the effect size value. Examples of effect sizes include the correlation between two variables, the regression coefficient in a regression, the mean difference, or the risk of a particular event (such as a heart attack) happening. Effect sizes complement statistical hypothesis testing, and play an important role in power analyses, sample size planning, and in meta-analyses. The cluster of data-analysis methods concerning effect sizes is referred to as estimation statistics. Effect size is an essential component when evaluating the strength of a statistical claim, and it is the first item (magnitude) in the MAGI ...
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External Links
An internal link is a type of hyperlink on a web page to another page or resource, such as an image or document, on the same website or domain. Hyperlinks are considered either "external" or "internal" depending on their target or destination. Generally, a link to a page outside the same domain or website is considered external, whereas one that points at another section of the same web page or to another page of the same website or domain is considered internal. These definitions become clouded, however, when the same organization operates multiple domains functioning as a single web experience, e.g. when a secure commerce website is used for purchasing things displayed on a non-secure website. In these cases, links that are "external" by the above definition can conceivably be classified as "internal" for some purposes. Ultimately, an internal link points to a web page or resource in the same root directory. Similarly, seemingly "internal" links are in fact "external" for ...
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