enow.com Web Search

Search results

  1. Results from the WOW.Com Content Network
  2. Deviance information criterion - Wikipedia

    en.wikipedia.org/wiki/Deviance_information_criterion

    The larger the effective number of parameters is, the easier it is for the model to fit the data, and so the deviance needs to be penalized. The deviance information criterion is calculated as D I C = p D + D ( θ ) ¯ , {\displaystyle \mathrm {DIC} =p_{D}+{\overline {D(\theta )}},}

  3. Deviance (statistics) - Wikipedia

    en.wikipedia.org/wiki/Deviance_(statistics)

    In statistics, deviance is a goodness-of-fit statistic for a statistical model; it is often used for statistical hypothesis testing.It is a generalization of the idea of using the sum of squares of residuals (SSR) in ordinary least squares to cases where model-fitting is achieved by maximum likelihood.

  4. Condition number - Wikipedia

    en.wikipedia.org/wiki/Condition_number

    Condition numbers can also be defined for nonlinear functions, and can be computed using calculus.The condition number varies with the point; in some cases one can use the maximum (or supremum) condition number over the domain of the function or domain of the question as an overall condition number, while in other cases the condition number at a particular point is of more interest.

  5. Mean signed deviation - Wikipedia

    en.wikipedia.org/wiki/Mean_signed_deviation

    The mean signed difference is derived from a set of n pairs, (^,), where ^ is an estimate of the parameter in a case where it is known that =. In many applications, all the quantities θ i {\displaystyle \theta _{i}} will share a common value.

  6. Deviation (statistics) - Wikipedia

    en.wikipedia.org/wiki/Deviation_(statistics)

    Absolute deviation in statistics is a metric that measures the overall difference between individual data points and a central value, typically the mean or median of a dataset. It is determined by taking the absolute value of the difference between each data point and the central value and then averaging these absolute differences. [ 4 ]

  7. Anomaly detection - Wikipedia

    en.wikipedia.org/wiki/Anomaly_detection

    Anomaly detection finds application in many domains including cybersecurity, medicine, machine vision, statistics, neuroscience, law enforcement and financial fraud to name only a few. Anomalies were initially searched for clear rejection or omission from the data to aid statistical analysis, for example to compute the mean or standard deviation.

  8. Algorithms for calculating variance - Wikipedia

    en.wikipedia.org/wiki/Algorithms_for_calculating...

    Algorithms for calculating variance play a major role in computational statistics.A key difficulty in the design of good algorithms for this problem is that formulas for the variance may involve sums of squares, which can lead to numerical instability as well as to arithmetic overflow when dealing with large values.

  9. Deficient number - Wikipedia

    en.wikipedia.org/wiki/Deficient_number

    Equivalently, it is a number for which the sum of proper divisors (or aliquot sum) is less than n. For example, the proper divisors of 8 are 1, 2, and 4, and their sum is less than 8, so 8 is deficient. Denoting by σ(n) the sum of divisors, the value 2n – σ(n) is called the number's deficiency.