Journal of the American Statistical Association · 1979 · 144 citations · 0 references
Natural SciencesData ScienceEconometricsStatistical ModelingStatisticsDensity EstimationHypothesis TestingInferential StatisticsNonparametric StatisticsStatistical AnalysisStatistical InferenceBox PlotsGoodness-of-fitGoodness-of-fit TestingGoodness-of-fit TestsNonparametric Hypothesis TestingParametric EstimationProbability Density FunctionsQuantile Regression
Abstract This article attempts to describe an approach to statistical data analysis which is simultaneously parametric and nonparametric. Given a random sample X 1, …, X n of a random variable X, one would like (1) to test the parametric goodness-of-fit hypothesis H 0 that the true distribution function F is of the form F(x) = F0[(x − μ)/σ)], where F 0 is specified, and (2) when H 0 is not accepted, to estimate nonparametrically the true density-quantile function fQ(u) and score function J(u) = − (fQ)'(u). The article also introduces density-quantile functions, autoregressive density estimation, estimation of location and scale parameters by regression analysis of the sample quantile function, and quantile-box plots.