npquantile  R Documentation 
npquantile
computes smooth quantiles from a univariate
unconditional kernel cumulative distribution estimate given data and,
optionally, a bandwidth specification i.e. a dbandwidth
object
using the bandwidth selection method of Li, Li and Racine (2017).
npquantile(x = NULL, tau = c(0.01,0.05,0.25,0.50,0.75,0.95,0.99), num.eval = 10000, bws = NULL, f = 1, ...)
x 
a univariate vector of type 
tau 
an optional vector containing the probabilities for quantile(s) to
be estimated (must contain numbers in [0,1]). Defaults to

num.eval 
an optional integer specifying the length of the grid on which the
quasiinverse is computed. Defaults to 
bws 
an optional 
f 
an optional argument fed to 
... 
additional arguments supplied to specify the bandwidth type, kernel
types, bandwidth selection methods, and so on. See

Typical usage is
x < rchisq(100,df=10) npquantile(x)
The quantile function q_τ is defined to be the leftcontinuous inverse of the distribution function F(x), i.e. q_τ = \inf\{x: F(x) ≥ τ\}.
A traditional estimator of q_τ is the τth sample
quantile. However, these estimates suffer from lack of efficiency
arising from variability of individual order statistics; see Sheather
and Marron (1990) and Hyndman and Fan (1996) for methods that
interpolate/smooth the order statistics, each of which discussed in
the latter can be invoked through quantile
via
type=j
, j=1,...,9
.
The function npquantile
implements a method for estimating
smooth quantiles based on the quasiinverse of a npudist
object where F(x) is replaced with its kernel estimator and
bandwidth selection is that appropriate for such objects; see
Definition 2.3.6, page 21, Nelsen 2006 for a definition of the
quasiinverse of F(x).
For construction of the quasiinverse we create a grid of evaluation
points based on the function extendrange
along with the
sample quantiles themselves computed from invocation of
quantile
. The coarseness of the grid defined by
extendrange
(which has been passed the option
f=1
) is controlled by num.eval
.
Note that for any value of τ less/greater than the smallest/largest value of F(x) computed for the evaluation data (i.e. that outlined in the paragraph above), the quantile returned for such values is that associated with the smallest/largest value of F(x), respectively.
npquantile
returns a vector of quantiles corresponding
to tau
.
Crossvalidated bandwidth selection is used by default
(npudistbw
). For large datasets this can be
computationally demanding. In such cases one might instead consider a
ruleofthumb bandwidth (bwmethod="normalreference"
) or,
alternatively, use kdtrees (options(np.tree=TRUE)
along with a
bounded kernel (ckertype="epanechnikov"
)), both of which will
reduce the computational burden appreciably.
Tristen Hayfield tristen.hayfield@gmail.com, Jeffrey S. Racine racinej@mcmaster.ca
Cheng, M.Y. and Sun, S. (2006), “Bandwidth selection for kernel quantile estimation,” Journal of the Chinese Statistical Association, 44, 271295.
Hyndman, R.J. and Fan, Y. (1996), “Sample quantiles in statistical packages,” American Statistician, 50, 361365.
Li, Q. and J.S. Racine (2017), “Smooth Unconditional Quantile Estimation,” Manuscript.
Li, C. and H. Li and J.S. Racine (2017), “CrossValidated Mixed Datatype Bandwidth Selection for Nonparametric Cumulative Distribution/Survivor Functions,” Econometric Reviews, 36, 970987.
Nelsen, R.B. (2006), An Introduction to Copulas, Second Edition, SpringerVerlag.
Sheather, S. and J.S. Marron (1990), “Kernel quantile estimators,” Journal of the American Statistical Association, Vol. 85, No. 410, 410416.
Yang, S.S. (1985), “A Smooth Nonparametric Estimator of a Quantile Function,” Journal of the American Statistical Association, 80, 10041011.
quantile
for various types of sample quantiles;
ecdf
for empirical distributions of which
quantile
is an inverse; boxplot.stats
and
fivenum
for computing other versions of quartiles;
qlogspline
for logspline density quantiles;
qkde
for alternative kernel quantiles, etc.
## Not run: ## Simulate data from a chisquare distribution df < 50 x < rchisq(100,df=df) ## Vector of quantiles desired tau < c(0.01,0.05,0.25,0.50,0.75,0.95,0.99) ## Compute kernel smoothed sample quantiles npquantile(x,tau) ## Compute sample quantiles using the default method in R (Type 7) quantile(x,tau) ## True quantiles based on known distribution qchisq(tau,df=df) ## End(Not run)
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