Safe Haskell	None

Data.Pass.L.Estimator

Synopsis

Documentation

data Estimator Source

Techniques used to smooth the nearest values when calculating quantile functions. R2 is used by default, and the numbering convention follows the use in the R programming language, as far as it goes.

Constructors

R1	Inverse of the empirical distribution function
R2	.. with averaging at discontinuities (default)
R3	The observation numbered closest to Np. NB: does not yield a proper median
R4	Linear interpolation of the empirical distribution function. NB: does not yield a proper median.
R5	.. with knots midway through the steps as used in hydrology. This is the simplest continuous estimator that yields a correct median
R6	Linear interpolation of the expectations of the order statistics for the uniform distribution on [0,1]
R7	Linear interpolation of the modes for the order statistics for the uniform distribution on [0,1]
R8	Linear interpolation of the approximate medans for order statistics.
R9	The resulting quantile estimates are approximately unbiased for the expected order statistics if x is normally distributed.
R10	When rounding h, this yields the order statistic with the least expected square deviation relative to p.
HD	The Harrell-Davis quantile estimator based on bootstrapped order statistics

Instances

Bounded Estimator
Enum Estimator
Eq Estimator
Data Estimator
Ord Estimator
Read Estimator
Show Estimator
Typeable Estimator
Binary Estimator
Hashable Estimator

data Estimate r Source

Constructors

Estimate !Rational (IntMap r)

Instances

Show r => Show (Estimate r)

estimateBy :: Fractional r => Estimator -> Rational -> Int -> Estimate rSource