When.com Web Search

Search results

  1. Results From The WOW.Com Content Network
  2. Hodges–Lehmann estimator - Wikipedia

    en.wikipedia.org/wiki/Hodges–Lehmann_estimator

    In the simplest case, the "Hodges–Lehmann" statistic estimates the location parameter for a univariate population. [2] [3] Its computation can be described quickly.For a dataset with n measurements, the set of all possible two-element subsets of it (,) such that ≤ (i.e. specifically including self-pairs; many secondary sources incorrectly omit this detail), which set has n(n + 1)/2 elements.

  3. Sample mean and covariance - Wikipedia

    en.wikipedia.org/wiki/Sample_mean_and_covariance

    The arithmetic mean of a population, or population mean, is often denoted μ. [2] The sample mean ¯ (the arithmetic mean of a sample of values drawn from the population) makes a good estimator of the population mean, as its expected value is equal to the population mean (that is, it is an unbiased estimator).

  4. Estimator - Wikipedia

    en.wikipedia.org/wiki/Estimator

    For example, the sample mean is a commonly used estimator of the population mean. There are point and interval estimators. The point estimators yield single-valued results. This is in contrast to an interval estimator, where the result would be a range of plausible values. "Single value" does not necessarily mean "single number", but includes ...

  5. Point estimation - Wikipedia

    en.wikipedia.org/wiki/Point_estimation

    We can then solve with the sample mean of the population moments. [10] However, due to the simplicity, this method is not always accurate and can be biased easily. Let (X 1, X 2,…X n) be a random sample from a population having p.d.f. (or p.m.f) f(x,θ), θ = (θ 1, θ 2, …, θ k). The objective is to estimate the parameters θ 1, θ 2 ...

  6. Ratio estimator - Wikipedia

    en.wikipedia.org/wiki/Ratio_estimator

    where N is the population size, n is the sample size, m x is the mean of the x variate and s x 2 and s y 2 are the sample variances of the x and y variates respectively. These versions differ only in the factor in the denominator (N - 1). For a large N the difference is negligible.

  7. Prediction interval - Wikipedia

    en.wikipedia.org/wiki/Prediction_interval

    If one makes the parametric assumption that the underlying distribution is a normal distribution, and has a sample set {X 1, ..., X n}, then confidence intervals and credible intervals may be used to estimate the population mean μ and population standard deviation σ of the underlying population, while prediction intervals may be used to estimate the value of the next sample variable, X n+1.

  8. Minimum-variance unbiased estimator - Wikipedia

    en.wikipedia.org/wiki/Minimum-variance_unbiased...

    However, the sample standard deviation is not unbiased for the population standard deviation – see unbiased estimation of standard deviation. Further, for other distributions the sample mean and sample variance are not in general MVUEs – for a uniform distribution with unknown upper and lower bounds, the mid-range is the MVUE for the ...

  9. Jackknife resampling - Wikipedia

    en.wikipedia.org/wiki/Jackknife_resampling

    This simple example for the case of mean estimation is just to illustrate the construction of a jackknife estimator, while the real subtleties (and the usefulness) emerge for the case of estimating other parameters, such as higher moments than the mean or other functionals of the distribution.