- smoothing methods
- Statsprocedures used in fitting a model to a set of statistical observations in a study, often by graphing the data to highlight its characteristics
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smoothing methods
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Smoothing — In statistics and image processing, to smooth a data set is to create an approximating function that attempts to capture important patterns in the data, while leaving out noise or other fine scale structures/rapid phenomena. Many different… … Wikipedia
Smoothing spline — The smoothing spline is a method of smoothing, or fitting a smooth curve to a set of noisy observations.DefinitionLet (x i,Y i); i=1,dots,n be a sequence of observations, modeled by the relation E(Y i) = mu(x i). The smoothing spline estimate… … Wikipedia
Methods used to study memory — The study of memory incorporates research methodologies from neuropsychology, human development and dragon testing using a wide range of species. The complex phenomenon of memory is explored by combining evidence from many areas of research. New… … Wikipedia
Meshfree methods — are a particular class of numerical simulation algorithms for the simulation of physical phenomena. Traditional simulation algorithms relied on a grid or a mesh, meshfree methods in contrast use the geometry of the simulated object directly for… … Wikipedia
Exponential smoothing — is a technique that can be applied to time series data, either to produce smoothed data for presentation, or to make forecasts. The time series data themselves are a sequence of observations. The observed phenomenon may be an essentially random… … Wikipedia
Savitzky–Golay smoothing filter — The Savitzky–Golay smoothing filter is a type of filter first described in 1964 by Abraham Savitzky and Marcel J. E. Golay. [A. Savitzky and Marcel J.E. Golay (1964). Smoothing and Differentiation of Data by Simplified Least Squares Procedures .… … Wikipedia
Kernel density estimation — of 100 normally distributed random numbers using different smoothing bandwidths. In statistics, kernel density estimation is a non parametric way of estimating the probability density function of a random variable. Kernel density estimation is a… … Wikipedia
Multivariate kernel density estimation — Kernel density estimation is a nonparametric technique for density estimation i.e., estimation of probability density functions, which is one of the fundamental questions in statistics. It can be viewed as a generalisation of histogram density… … Wikipedia
n-gram — Not to be confused with engram. In the fields of computational linguistics and probability, an n gram is a contiguous sequence of n items from a given sequence of text or speech. The items in question can be phonemes, syllables, letters, words or … Wikipedia
Kernel regression — Not to be confused with Kernel principal component analysis. The kernel regression is a non parametric technique in statistics to estimate the conditional expectation of a random variable. The objective is to find a non linear relation between a… … Wikipedia
Complémentarité linéaire — En mathématiques, et plus spécialement en recherche opérationnelle et en optimisation, un problème de complémentarité linéaire est défini par la donnée d une matrice et d un vecteur et consiste à trouver un vecteur tel que ses composantes et… … Wikipédia en Français
