On Non-Parametric Estimates of Density Functions and Regression Curves

1965 ◽  
Vol 10 (1) ◽  
pp. 186-190 ◽  
Author(s):  
É. A. Nadaraya
Keyword(s):  
Density Functions ◽  
Regression Curves ◽  
Non Parametric

Non-parametric e-mixture of Density Functions

2016 ◽  
pp. 3-10 ◽  
Author(s):  
Hideitsu Hino ◽  
Ken Takano ◽  
Shotaro Akaho ◽  
Noboru Murata
Keyword(s):  
Density Functions ◽  
Non Parametric

Non-parametric -sample tests: Density functions vs distribution functions

2009 ◽  
Vol 53 (9) ◽  
pp. 3344-3357 ◽  
Author(s):  
Pablo Martínez-Camblor ◽  
Jacobo de Uña-Álvarez
Keyword(s):  
Distribution Functions ◽  
Density Functions ◽  
Non Parametric

scholarly journals Prediction of record values by using quantile regression curves and distortion functions

Metrika ◽  
2021 ◽  
Author(s):  
Jorge Navarro
Keyword(s):  
Record Values ◽  
Confidence Bands ◽  
Parametric Models ◽  
Dependence Structure ◽  
Data Set ◽  
Conditional Quantile ◽  
Regression Curves ◽  
Distortion Functions ◽  
Distorted Distributions ◽  
Non Parametric

AbstractThe purpose of the paper is to provide a general method based on conditional quantile curves to predict record values from preceding records. The predictions are based on conditional median (or median regression) curves. Moreover, conditional quantiles curves are used to provide confidence bands for these predictions. The method is based on the recently introduced concept of multivariate distorted distributions that are used instead of copulas to represent the dependence structure. This concept allows us to compute the conditional quantile curves in a simple way. The theoretical findings are illustrated with a non-parametric model (standard uniform), two parametric models (exponential and Pareto), and a non-parametric procedure for the general case. A real data set and a simulated case study in reliability are analysed.

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