Empirical Distribution Function - Definition

Definition

Let (x1, …, xn) be iid real random variables with the common cdf F(t). Then the empirical distribution function is defined as

 \hat F_n(t) = \frac{ \mbox{number of elements in the sample} \leq t}n =
\frac{1}{n} \sum_{i=1}^n \mathbf{1}\{x_i \le t\},

where 1{A} is the indicator of event A. For a fixed t, the indicator 1{xit} is a Bernoulli random variable with parameter p = F(t), hence is a binomial random variable with mean nF(t) and variance nF(t)(1 − F(t)). This implies that is an unbiased estimator for F(t).

Read more about this topic:  Empirical Distribution Function

Famous quotes containing the word definition:

    Scientific method is the way to truth, but it affords, even in
    principle, no unique definition of truth. Any so-called pragmatic
    definition of truth is doomed to failure equally.
    Willard Van Orman Quine (b. 1908)

    One definition of man is “an intelligence served by organs.”
    Ralph Waldo Emerson (1803–1882)

    Beauty, like all other qualities presented to human experience, is relative; and the definition of it becomes unmeaning and useless in proportion to its abstractness. To define beauty not in the most abstract, but in the most concrete terms possible, not to find a universal formula for it, but the formula which expresses most adequately this or that special manifestation of it, is the aim of the true student of aesthetics.
    Walter Pater (1839–1894)