# Kernel regression

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## 노트

### 위키데이터

- ID : Q1739319

### 말뭉치

- In statistics, Kernel regression is a non-parametric technique to estimate the conditional expectation of a random variable.
^{[1]} - the function npreg of the np package can perform kernel regression.
^{[1]} - This leads to the technique known as kernel regression.
^{[2]} - This gives us a mathematical justification for using kernel regression in cases where it is possible to do so.
^{[2]} - On the down side, since we need to calculate the Gram matrix, kernel regression does not scale well – for large datasets turning to neural networks is a better idea.
^{[2]} - Bandwidth in kernel regression is called the smoothing parameter because it controls variance and bias in the output.
^{[3]} - In this example, a kernel regression model is developed to predict river flow from catchment area.
^{[3]} - In this example, a bandwidth value of 10 is used to explain kernel regression.
^{[3]} - The fundamental calculation behind kernel regression is to estimate weighted sum of all observed y values for a given predictor value, xi.
^{[3]} - Let’s see an application of multivariate kernel regression for the wine.csv dataset.
^{[4]} - Using only the blue data points, Gaussian Kernel Regression arrives at the approximated function given by the red line.
^{[5]} - The above equation is the formula for what is more broadly known as Kernel Regression.
^{[5]} - In robust nonparametric kernel regression context, we prescribe method to select trimming parameter and bandwidth.
^{[6]} - Kernel regression is an estimation technique to fit your data.
^{[7]} - The idea of kernel regression is putting a set of identical weighted function called Kernel local to each observational data point.
^{[7]} - In Kernel regression, what you do is to put a kernel (a kind of bump function) to each point of your X data.
^{[7]} - A SAS programmer recently asked me how to compute a kernel regression in SAS.
^{[8]} - "What is loess regression" and "Loess regression in SAS/IML" and was trying to implement a kernel regression in SAS/IML as part of a larger analysis.
^{[8]} - This article explains how to create a basic kernel regression analysis in SAS.
^{[8]} - A kernel regression smoother is useful when smoothing data that do not appear to have a simple parametric relationship.
^{[8]} - Kernel regression is a modeling tool which belongs to the family of smoothing methods.
^{[9]} - Unlike linear regression which is both used to explain phenomena and for prediction (understanding a phenomenon to be able to predict it afterwards), Kernel regression is mostly used for prediction.
^{[9]} - In this paper, we first review the latest developments of sparse metric learning and kernel regression.
^{[10]} - Then a novel kernel regression method involving sparse metric learning, which is called kernel regression with sparse metric learning (KR_SML), is proposed.
^{[10]} - The sparse kernel regression model is established by enforcing a mixed (2,1)-norm regularization over the metric matrix.
^{[10]} - Our work is the first to combine kernel regression with sparse metric learning.
^{[10]} - I am using kernel regression for build a relationship between independent and dependent variables in development period data.
^{[11]} - Train a default Gaussian kernel regression model with the standardized predictors.
^{[12]} - Below you will find a range of commented examples intended to help you become familiar with applied nonparametric kernel regression in R/RStudio using the np package.
^{[13]} - As in kernel density estimation, kernel regression involves choosing the kernel function and the bandwidth parameter.
^{[14]}

### 소스

- ↑
^{1.0}^{1.1}Kernel regression - ↑
^{2.0}^{2.1}^{2.2}Advanced Machine Learning: Basics and Kernel Regression - ↑
^{3.0}^{3.1}^{3.2}^{3.3}Kernel Regression — with example and code - ↑ Notes for Nonparametric Statistics
- ↑
^{5.0}^{5.1}Kernel Regression · Chris McCormick - ↑ Robust nonparametric kernel regression estimator
- ↑
^{7.0}^{7.1}^{7.2}Kernel Regression - ↑
^{8.0}^{8.1}^{8.2}^{8.3}Kernel regression in SAS - ↑
^{9.0}^{9.1}Nonparametric regression (Kernel and Lowess) - ↑
^{10.0}^{10.1}^{10.2}^{10.3}Kernel regression with sparse metric learning - ↑ Validation of Kernel Regression
- ↑ Fit Gaussian kernel regression model using random feature expansion
- ↑ Kernel regression examples using np
- ↑ 6.2 Kernel Regression

## 메타데이터

### 위키데이터

- ID : Q1739319