Nonparametric regression is a category of regression analysis in which the predictor does not take a predetermined form but is constructed according to...
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models (e.g., nonparametric regression). Regression analysis is primarily used for two conceptually distinct purposes. First, regression analysis is widely...
36 KB (5,081 words) - 16:47, 16 February 2024
non-linear relation between a pair of random variables X and Y. In any nonparametric regression, the conditional expectation of a variable Y {\displaystyle Y}...
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method to estimate a probability distribution. Nonparametric regression and semiparametric regression methods have been developed based on kernels, splines...
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In statistics, semiparametric regression includes regression models that combine parametric and nonparametric models. They are often used in situations...
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Linear regression Simple linear regression Logistic regression Nonlinear regression Nonparametric regression Robust regression Stepwise regression Regression...
2 KB (235 words) - 14:22, 18 October 2023
Local regression or local polynomial regression, also known as moving regression, is a generalization of the moving average and polynomial regression. Its...
18 KB (2,557 words) - 14:15, 17 January 2024
Linear equation Logistic regression M-estimator Multivariate adaptive regression spline Nonlinear regression Nonparametric regression Normal equations Projection...
70 KB (9,686 words) - 05:47, 13 May 2024
represents an improved technique in the neural networks based on the nonparametric regression. The idea is that every training sample will represent a mean to...
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data (e.g. using ordinary least squares). Nonparametric regression refers to techniques that allow the regression function to lie in a specified set of functions...
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In statistics and numerical analysis, isotonic regression or monotonic regression is the technique of fitting a free-form line to a sequence of observations...
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quantile and nonparametric estimators have also been developed. These and other censored regression models are often confused with truncated regression models...
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In statistics, polynomial regression is a form of regression analysis in which the relationship between the independent variable x and the dependent variable...
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"multilevel regression" and "poststratification" ideas of MRP can be generalized. Multilevel regression can be replaced by nonparametric regression or regularized...
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doi:10.1007/978-0-387-84858-7, [1] (eq.(5.16)) Fox, J. (2000). Nonparametric Simple Regression: Smoothing Scatterplots. Quantitative Applications in the Social...
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adaptive regression splines (MARS) is a form of regression analysis introduced by Jerome H. Friedman in 1991. It is a non-parametric regression technique...
22 KB (3,136 words) - 19:29, 14 October 2023
Quantile regression is a type of regression analysis used in statistics and econometrics. Whereas the method of least squares estimates the conditional...
29 KB (4,067 words) - 17:49, 1 May 2024
Kernel (statistics) (category Nonparametric statistics)
classification, regression analysis, and cluster analysis on data in an implicit space. This usage is particularly common in machine learning. In nonparametric statistics...
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Additive model (category Nonparametric regression)
In statistics, an additive model (AM) is a nonparametric regression method. It was suggested by Jerome H. Friedman and Werner Stuetzle (1981) and is an...
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models Nonparametric regression Isotonic regression Semiparametric regression Local regression Total least squares regression Deming regression Errors-in-variables...
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Passing–Bablok regression is a method from robust statistics for nonparametric regression analysis suitable for method comparison studies introduced by...
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Ridge regression is a method of estimating the coefficients of multiple-regression models in scenarios where the independent variables are highly correlated...
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Smoothing spline (redirect from Spline regression)
Boor's official site [1]. Green, P. J.; Silverman, B.W. (1994). Nonparametric Regression and Generalized Linear Models: A roughness penalty approach. Chapman...
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case, the "regression" effect is statistically likely to occur, but in the second case, it may occur less strongly or not at all. Regression toward the...
40 KB (5,609 words) - 02:32, 9 March 2024
In statistics, ordinal regression, also called ordinal classification, is a type of regression analysis used for predicting an ordinal variable, i.e....
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combination of one or more independent variables. In regression analysis, logistic regression (or logit regression) is estimating the parameters of a logistic...
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for his work on data visualization, particularly on nonparametric regression and local regression. He is remembered as one of the developers of the S...
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Poisson regression is a generalized linear model form of regression analysis used to model count data and contingency tables. Poisson regression assumes...
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In statistics, simple linear regression (SLR) is a linear regression model with a single explanatory variable. That is, it concerns two-dimensional sample...
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Lasso (statistics) (redirect from Lasso regression)
linear regression models. This simple case reveals a substantial amount about the estimator. These include its relationship to ridge regression and best...
48 KB (7,736 words) - 05:16, 24 May 2024