Quantile Function
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High Quality Content by WIKIPEDIA articles! In probability theory, a quantile function of a probability distribution is the inverse F ?1 of its cumulative distribution function (cdf) F. Assuming a continuous and strictly monotonic distribution function, scriptstyle Fcolon R to (0,1), the quantile function returns the value below which random draws from the given distribution would fall, p×100 percent of the time. Quantile functions are used in both statistical applications and Monte-Carlo methods. For statistical applications, users need to know key percentage points of a given distribution. ...
High Quality Content by WIKIPEDIA articles! In probability theory, a quantile function of a probability distribution is the inverse F ?1 of its cumulative distribution function (cdf) F. Assuming a continuous and strictly monotonic distribution function, scriptstyle Fcolon R to (0,1), the quantile function returns the value below which random draws from the given distribution would fall, p×100 percent of the time. Quantile functions are used in both statistical applications and Monte-Carlo methods. For statistical applications, users need to know key percentage points of a given distribution. For example, they require the median and 25% and 75% quartiles as in the example above or 5%, 95%, 2.5%, 97.5% levels for other applications such as assessing the statistical significance of an observation whose distribution is known; see the quantile entry. Statistical applications of quantile functions are discussed extensively by Gilchrist (2000).
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