Nonparametric Statistical Data Modeling

By Emanuel Parzen. 1979

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Citations in the corpus

Autoregressive Quantile Networks for Generative Modeling (2018, background): “Indeed, this quantity, known as the sparsity function (Tukey, 1965) or the quantile-density function (Parzen, 1979) plays a central role in the analysis of quantile regression models (Koenker, 1994).”
Violin plots : A box plot-density trace synergism (1998, methodology): “First, Parzen (1979) and Scott (1992) used annual snowfall data for Buffalo, New York for 19101972 to show the value of nonparametric density estimation.”
Design of Experiments in Nonlinear Models: Asymptotic Normality, Optimality Criteria and Small-Sample Properties (2013, background): “see Pázman (1993b) for a detailed derivation of this expression, we have Cint(X, θ;u) = 0 at any point θ ∈ int(Θ) and in any direction u ∈ R.”
UNIFORM BIAS STUDY AND BAHADUR REPRESENTATION FOR LOCAL POLYNOMIAL ESTIMATORS OF THE CONDITIONAL QUANTILE FUNCTION (2011, background): “The considered estimator of q(α|x) is a conditional version of the Parzen (1979) convolution estimator, q̂(α|x) = 1 hq ∫ Q̂h(a|x)d Kq ( a −α hq ) = 1 hq ∫ Q̂h(α +hqt |x)d Kq (t) ; (3.”
Probabilistic index models (2012, unknown): “Along with FEV (litres), the covariates AGE (years), HEIGHT (inches), SEX and SMOKING status (1 if the child smokes; 0 if the child does not smoke) are provided for 654 children of ages 3–19 years. See Rosner (1999), page 41, for more information.”
The Big Data Newsvendor: Practical Insights from Machine Learning (2013, background): “The values of f ( F − 1 ( y )) and J ( y ) for the distributions in Table 1 can be found in Parzen (1979).”
Quartiles in Elementary Statistics (2006, methodology): “This method is Definition 4 of Hyndman and Fan (1996) who refer to Parzen (1979) and is Method 1 of Wessa (2006).”
Smoothing Quantile Regressions (2019, unknown): “In particular, Nadaraya (1964) and Parzen (1979) estimated unconditional quantiles, respectively, by inverting a smoothed estimator of the cumulative distribution function (cdf) and by smoothing the sample quantile function.”
ESTIMATING DENSITIES , QUANTILES , QUANTILE DENSITIES AND DENSITY QUANTILES (2004, methodology): “We follow Parzen's (1979) terminology in calling q the quantile density function; Tukey (1965) called it the sparsity function.”
Forecasting of density functions with an application to cross-sectional and intraday returns (2019, background): “Of these three, Qt is the least constrained, and it is easy to see that the so-called quantile density (Jones, 1992; Parzen, 1979; Tukey, 1965) qt = Q ′t is only constrained to be nonnegative.”