nonparametric procedure - definition. What is nonparametric procedure
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CATEGORY OF REGRESSION ANALYSIS
Nonparametric multiplicative regression; Non-parametric regression; Nonparametric Regression
  •  Example of a curve (red line) fit to a small data set (black points) with nonparametric regression using a Gaussian kernel smoother. The pink shaded area illustrates the kernel function applied to obtain an estimate of y for a given value of x. The kernel function defines the weight given to each data point in producing the estimate for a target point.

Credé's prophylaxis         
MEDICAL PROCEDURE PERFORMED ON NEWBORNS
Crede procedure; Credé procedure
Credé procedure is the practice of washing a newborn's eyes with a 2% silver nitrate solution to protect against neonatal conjunctivitis caused by Neisseria gonorrhoeae.
Radiotelephony procedure         
METHODS TO MAKE VOICE COMMUNICATIONS UNDERSTOOD OVER A POTENTIALLY DEGRADED CHANNEL
Radio language; Communications discipline; Voice procedure; Radiotelephony voice procedure
Radiotelephony procedure (also on-air protocol and voice procedure) includes various techniques used to clarify, simplify and standardize spoken communications over two-way radios, in use by the armed forces, in civil aviation, police and fire dispatching systems, citizens' band radio (CB), and amateur radio.
Nonparametric regression         
Nonparametric regression is a category of regression analysis in which the predictor does not take a predetermined form but is constructed according to information derived from the data. That is, no parametric form is assumed for the relationship between predictors and dependent variable.

ويكيبيديا

Nonparametric regression

Nonparametric regression is a category of regression analysis in which the predictor does not take a predetermined form but is constructed according to information derived from the data. That is, no parametric form is assumed for the relationship between predictors and dependent variable. Nonparametric regression requires larger sample sizes than regression based on parametric models because the data must supply the model structure as well as the model estimates.