enow.com Web Search

Search results

  1. Results from the WOW.Com Content Network
  2. Propensity score matching - Wikipedia

    en.wikipedia.org/wiki/Propensity_score_matching

    SPSS: A dialog box for Propensity Score Matching is available from the IBM SPSS Statistics menu (Data/Propensity Score Matching), and allows the user to set the match tolerance, randomize case order when drawing samples, prioritize exact matches, sample with or without replacement, set a random seed, and maximize performance by increasing ...

  3. Matching (statistics) - Wikipedia

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

    Matching is a statistical technique that evaluates the effect of a treatment by comparing the treated and the non-treated units in an observational study or quasi-experiment (i.e. when the treatment is not randomly assigned).

  4. Inverse probability weighting - Wikipedia

    en.wikipedia.org/wiki/Inverse_probability_weighting

    An alternative estimator is the augmented inverse probability weighted estimator (AIPWE) combines both the properties of the regression based estimator and the inverse probability weighted estimator. It is therefore a 'doubly robust' method in that it only requires either the propensity or outcome model to be correctly specified but not both.

  5. Kernel density estimation - Wikipedia

    en.wikipedia.org/wiki/Kernel_density_estimation

    Kernel density estimation of 100 normally distributed random numbers using different smoothing bandwidths.. In statistics, kernel density estimation (KDE) is the application of kernel smoothing for probability density estimation, i.e., a non-parametric method to estimate the probability density function of a random variable based on kernels as weights.

  6. Gillespie algorithm - Wikipedia

    en.wikipedia.org/wiki/Gillespie_algorithm

    Partial-propensity formulations, as developed independently by both Ramaswamy et al. (2009, 2010) and Indurkhya and Beal (2010), are available to construct a family of exact versions of the algorithm whose computational cost is proportional to the number of chemical species in the network, rather than the (larger) number of reactions.

  7. Simple matching coefficient - Wikipedia

    en.wikipedia.org/wiki/Simple_matching_coefficient

    Given two objects, A and B, each with n binary attributes, SMC is defined as: = = + + + +. where is the total number of attributes where A and B both have a value of 0,; is the total number of attributes where A and B both have a value of 1,

  8. Template:NuclidesDivided, 0-29 - Wikipedia

    en.wikipedia.org/wiki/Template:NuclidesDivided,_0-29

    Main page; Contents; Current events; Random article; About Wikipedia; Contact us; Pages for logged out editors learn more

  9. Loss functions for classification - Wikipedia

    en.wikipedia.org/wiki/Loss_functions_for...

    Given the binary nature of classification, a natural selection for a loss function (assuming equal cost for false positives and false negatives) would be the 0-1 loss function (0–1 indicator function), which takes the value of 0 if the predicted classification equals that of the true class or a 1 if the predicted classification does not match ...