Data smoothing algorithms, Advanced Statistics

The procedures for extracting the pattern in a series of observations when this is obscured by the noise. Basically any such technique or method separates the original series into a smooth sequence and the residual sequence (usually called the 'rough'). For instance, a smoother can separate seasonal Fluctuations from the briefer events such as identifiable peaks and random noise. A simple example of such a process is the moving average; a more complex one is locally weighted regression.

Posted Date: 7/27/2012 2:02:49 AM | Location : United States







Related Discussions:- Data smoothing algorithms, Assignment Help, Ask Question on Data smoothing algorithms, Get Answer, Expert's Help, Data smoothing algorithms Discussions

Write discussion on Data smoothing algorithms
Your posts are moderated
Related Questions
5. Packages from a machine a normally distributed with a mean 200g and its standard deviation 2grams. Find the probability that a package from the machine weighs a) Less than

Kurtosis: The extent to which the peak of the unimodal probability distribution or the frequency distribution departs from its shape of the normal distribution, by either being mo

Odds ratio is the ratio of the odds for the binary variable in two groups of the subjects, such as, males and females. If the two possible states of variable are labeled as 'succe

Recursive models are the statistical models in which the causality flows in one direction, that is models which include only unidirectional effects. Such type of models do not inc

Software which started out as the spreadsheet targeting at manipulating the tables of number for financial analysis, which has now developed into a more flexible package for workin

There is high level of fluctuation in a zigzag pattern in the time series for RESI1 which indicates that there is possibly negative autocorrelation present. Column C11 show

Nearest-neighbour methods are the methods of discriminant analysis are based on studying the training set subjects much similar to the subject to be classified. Classification mig

The Null Hypothesis - H0: β 1 = 0 i.e. there is homoscedasticity errors and no heteroscedasticity exists The Alternative Hypothesis - H1: β 1 ≠ 0 i.e. there is no homoscedasti

Multiple imputation : The Monte Carlo technique in which missing values in the data set are replaced by m> 1 simulated versions, where m is usually small (say 3-10). Each of simula

A term usually used for unobserved individual heterogeneity. Such variation is of main concern in the medical statistics particularly in the analysis of the survival times where ha