The kernel tag has no wiki summary.
5
votes
1answer
89 views
Kernel density estimation on asymmetric distributions
Let $\{s_1,\ldots,s_N\}$ be a set of samples drawn from an unknown (but certainly asymmetric) probability distribution.
I would like to find the probability distribution by using the KDE approach:
$$
...
2
votes
1answer
81 views
Mixed SVM kernel of RBF and linear
I've read some introduction about different kernels for SVM. It seems RBF is a measure of point distance while the basic kernel (i.e. no kernel) splits the space by hyper-planes.
I could imagine that ...
1
vote
1answer
30 views
Evaluating features and similarity measures
I am currently developing a classificator, which is supposed to classify into a number of classes. For this purpose I am
designing some features and similarity measures which I might use for a later ...
4
votes
0answers
232 views
How can I convert kernel quantiles into sample quantiles?
I calculated the quantiles for an Epanechnikov kernel which I'm using to estimate the density of a sample. What I need is to find the sample quantiles knowing that it is composed of many Epanechnikov ...
3
votes
0answers
22 views
Kernel density estimator that doesn't collapse in the tails
I have iid datapoints $x_1, \dots, x_n$, generated by an unknown density $f(x)$.
So far I have approximated $f(x)$ with a normal $N(\hat{\mu}, \hat{\sigma}^2 )$, where $\hat{\mu}$ and $\hat{\sigma}^2$ ...
2
votes
0answers
38 views
How can I find the number of support vectors in an SVM, depending on kernel?
Here on slide 3 I see the claim that for linearly separable cases the number of support vectors will be d+1, where d is the dimension we are working on.
What if my data is not linearly separable and ...
2
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0answers
67 views
local polynomial regression standard errors
I am attempting to find a reference which explains how one computes standard errors for local polynomial regression? Specifically, in R one can use the loess ...
2
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0answers
93 views
Representation within a RKHS framework
Given a p.s.d kernel $Q$, can minimization/maximization of $Tr(X^TQX)$ over X be represented within a reproducing kernel Hilbert space (RKHS) framework? If there is a primary concern with the trace ...
2
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0answers
136 views
Regarding kernel-based naive Bayesian classifier
Are there any good references for kernel-based Naive Bayesian classifier?
2
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0answers
78 views
Bandwidth selection for smooth reliability diagram
Following up on "How to evaluate quality of probability estimator for Bernoulli experiments?", I want to visualize the quality of an estimator for probability forecasting using a Reliability Diagram.
...
2
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0answers
161 views
How to explain certain patterns appearing after kernel averaging?
Having a 2D map filled uniformly by random values (Figure:top-left), the next maps are kernel averaged with a kernel of sizes ...
2
votes
0answers
127 views
About kernel based estimates
Kernel based operations are common in a variety of applications, such as image processing (e.g., blurring), generating smoothed estimation maps, and so on. A common approach is to select four ...
2
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0answers
392 views
Conditional kernel density plot with R's np package
I tried to use the Kernel Density plot method from Hayfield and Racine (2008) np package for my own data, but somehow ended up with different type of plots and I ...
1
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0answers
47 views
How to choose kernel functions for support vector regression
Are there any good resources regarding how to design kernels for regression problems, specifically time-series regression type of problem. I am finding the choice of a kernel for regression extremely ...
1
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0answers
83 views
Weighted covariance matrix using kernels
I would like to create a weighted covariance matrix (say 5 variables) using 3 different time points where the weights come from a kernel function (can be normal, triangular, etc) but I'm not ...
1
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0answers
54 views
Computing weighted standard deviation using lowess mean values
I have two questions:
First question:
I want to compute the weighted standard deviation with tri-cubic kernel. I am using lowess function in R to compute the weighted mean using tri-cubic ...
1
vote
0answers
73 views
Sheather-Jones bandwidth algorithm implementation in java?
The Sheather-Jones method for selecting an appropriate bandwidth for kernel density estimation generally produces better results than simpler methods such as Silverman's rule of thumb and Scott's ...
1
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0answers
40 views
what can I say about the following kernel estimation?
I have a discrete random variable $X$ and two random vectors $\vec{Y}$ and $\vec{Z}$.
For a given $x$ (i.e. an instance of $X$), I am interested in estimating:
$$E[\vec{Y} \vec{Z}^{\top} | X = x]$$
...
1
vote
0answers
166 views
Which non-parametric regression could I apply to fit a curve to this data set?
I have posted a similar question about the same problem, having been suggested to use a polynomial Robust Linear Model, which worked fine for most cases, as can be seen here:
Non-algebric ...
1
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0answers
46 views
Given a kernel, how to find mapping phi?
I'm not clear about kernel.
How could I construct my own kernel that is valid? Is the only method Merce Theorem (positive semi-definite)? I mean if I know K is a valid kernel, do I know that ...
1
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0answers
54 views
Polynomial kernel in logistic regression?
So I have put together a nice logistic regression program that works quite well. Now, I have used two dimensions to test it and see how it works, and guided by some online tutorials, have increased ...
1
vote
0answers
87 views
How to estimate the intensity of a multidimensional point process?
Note that for a homogeneous point process the density is just a number, while for an in-homogeneous process it is a function. In addition, how can I distribute that function on a larger study region ...
1
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0answers
117 views
Multivariate non-parametric density estimation with many missing values
Apologies in advance if any of my terminology here is wrong, I'm not an expert in statistics. If I've made any mistakes, let me know and I'll correct them.
The task I'm looking for some advice on ...
1
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0answers
170 views
Behavior of a sum of kernel functions
Suppose we have 2 kernel functions $K_1(x,y)$ and $K_2(x,y)$. We know, that the dataset ($(x_1,y_1),\ldots,(x_l,y_l),$ $y_i \in \{-1,1\}$ ) is separated with the first one (that is, there are $w,$ ...
1
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0answers
35 views
Approach to testing difference from a bivariate null distribution generated by randomization
I would like to test if each of the red observations is more extreme in variable xy[,2] than 95% of a null hypothesis (black dots) generated by randomization.
I am ...
1
vote
0answers
322 views
What's the best Kernel Regression package in R?
I am looking for a good and modern Kernel Regression package in R, which has the following features:
It has cross-validation
It can automatically choose the "optimal" bandwidth
It doesn't have ...
0
votes
0answers
19 views
Smoothing of log-distributed periodogram
I use the lomb-scargle periodogram to output information about chemical species in distinct time periods. This produces a distribution that is skewed heavily, with the majority of points and variance ...
0
votes
0answers
23 views
intersection kernel and distances between two histograms
intersection kernel can be given as $\sum_i min(x_i, y_i)$ . where x and y are histograms.
If two histograms are compeletely different the distance will be low.
If two histograms are similar what ...
0
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0answers
46 views
Estimating probability density in Parzen windows
I came across an interesting paper about stability measure which can be used as evaluation metric for continuous data discretization.
The stability measure is constructed from a series of estimated ...
0
votes
0answers
29 views
What distribution to choose? Do Kernel estimations help?
I recently did some maximum likelihood estimations where the dependent variable justified the use of a normal distribution. Now, however, the dependent variable has a skewness of 0.4 and a kurtosis of ...
0
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0answers
95 views
SVM with svm and svmpath function
I am trying to compare the R functions svm (library: e1071) and svmpath (library svmpath).
...
0
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0answers
60 views
Difference between Hpi.kfe, Hpi and Hscv in R's 'ks' package?
There are three bandwidth selector functions programmed in the ks package: Hpi, Hscv and Hpi.kfe, what is the difference between them?
And what is the difference between a plug-in and a smoothed ...
0
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0answers
196 views
SVM using RBF and nearest neighbor classification method
SVM using RBF kernel is claimed to be similar (equivalent) to the K nearest neighbor classification method. I am not very clear about the analysis process of building this kind of relationship. Thanks ...
-1
votes
0answers
23 views
kernels distances gram matrix classification
Could you please explain some thing about kernels? As I understand it is technique to map the feature space into a high dimensional feature space where we could separate two classes by a linear ...
