Description: The subroutines glkern.f and lokern.f use an efficient and fast algorithm for
automatically adaptive nonparametric regression estimation with a kernel method.
Roughly speaking, the method performs a local averaging of the observations when
estimating the regression function. Analogously, one can estimate derivatives of
small order of the regression function. Platform: |
Size: 194910 |
Author:zhanglifang |
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Description: The subroutines glkern.f and lokern.f use an efficient and fast algorithm for
automatically adaptive nonparametric regression estimation with a kernel method.
Roughly speaking, the method performs a local averaging of the observations when
estimating the regression function. Analogously, one can estimate derivatives of
small order of the regression function. Platform: |
Size: 194560 |
Author:zhanglifang |
Hits:
Description: 非参数统计学中非参数回归的简单应用核回归程序，应用范围广泛，不需要知道样本的分布就可以使用该方法。-Non-parametric statistical regression Nonparametric kernel regression of the simple application procedure, a wide range of applications, does not need to know the distribution of the samples you can use this method. Platform: |
Size: 2048 |
Author:林森 |
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Description: Gaussian的小波去噪工具箱，基于matlab-Gaussian Wavelet Denoising Matlab Toolbox
Various wavelet shrinkage and wavelet thresholding estimators, appeared in the nonparametric regression literature, are implemented in MATLAB§. These estimators arise from a wide range of classical and empirical Bayes methods treating either individual or blocks of wavelet coefficients. Platform: |
Size: 294912 |
Author:hanyue |
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Description: In this paper, we make contact with the field of nonparametric
statistics and present a development and generalization
of tools and results for use in image processing and reconstruction.
In particular, we adapt and expand kernel regression ideas
for use in image denoising, upscaling, interpolation, fusion, and
more. Furthermore, we establish key relationships with some popular
existing methods and show how several of these algorithms,
including the recently popularized bilateral filter, are special cases
of the proposed framework. The resulting algorithms and analyses
are amply illustrated with practical examples. Platform: |
Size: 9319424 |
Author:ionutmirel |
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Description: The ability of a regression tree method to properly
interpolate among recorded data to give an estimate of the frequency
decline following a generator outage is examined in this
letter. The proposed method is a nonparametric technique that
can select those system characteristics and their interactions that
are most important in determining the relation between the generation/
load imbalance and the frequency decline. The information
obtained from the proposed method can be used online for
scheduling fast-acting reserve or load shedding for severe generator
outage incidents Platform: |
Size: 155648 |
Author:jorgehas |
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Description: 支持向量机工具箱By Kris De Brabanter,标准的非参数回归，健壮的回归，一些调优标准等经典交叉验证，较好的交互性-The StatLSSVM toolbox is written so that only a few lines of code are necessary in order to perform standard nonparametric regression, regression with correlated errors and robust regression. In addition, construction of additive models and pointwise or uniform confidence intervals are also supported. A number of tuning criteria such as classical cross-validation, robust cross-validation and cross-validation for correlated errors are available. Also, minimization of the previous criteria is available without any user interaction. Platform: |
Size: 326656 |
Author:李杰 |
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Description: Curve Fitting Toolbox Product Description
Fit curves and surfaces to data using regression, interpolation, and smoothing
Curve Fitting Toolbox™ provides an app and functions for fitting curves and surfaces to
data. The toolbox lets you perform exploratory data analysis, preprocess and post-process
data, compare candidate models, and remove outliers. You can conduct regression
analysis using the library of linear and nonlinear models provided or specify your
own custom equations. The library provides optimized solver parameters and starting
conditions to improve the quality of your fits. The toolbox also supports nonparametric
modeling techniques, such as splines, interpolation, and smoothing.
After creating a fit, you can apply a variety of post-processing methods for plotting,
interpolation, and extrapolation estimating confidence intervals and calculating-Curve Fitting Toolbox Product Description
Fit curves and surfaces to data using regression, interpolation, and smoothing
Curve Fitting Toolbox™ provides an app and functions for fitting curves and surfaces to
data. The toolbox lets you perform exploratory data analysis, preprocess and post-process
data, compare candidate models, and remove outliers. You can conduct regression
analysis using the library of linear and nonlinear models provided or specify your
own custom equations. The library provides optimized solver parameters and starting
conditions to improve the quality of your fits. The toolbox also supports nonparametric
modeling techniques, such as splines, interpolation, and smoothing.
After creating a fit, you can apply a variety of post-processing methods for plotting,
interpolation, and extrapolation estimating confidence intervals and calculating Platform: |
Size: 10616832 |
Author:maryam |
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Description: 局部加权多项式回归的目的是解决全球行为模型的表现不好或不能有效地应用于不必要的努力。LWP是一种非参数回归方法，是通过低阶多项式对数据子集进行逐点拟合局部。(Locally Weighted Polynomial regression is designed to address situations in which models of global behaviour do not perform well or cannot be effectively applied without undue effort. LWP is a nonparametric regression method that is carried out by pointwise fitting of low-degree polynomials to localized subsets of the data.) Platform: |
Size: 4096 |
Author:baidudu |
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Search - Nonparametric regression