kernel density estimation
0
votes
1answer
50 views
Confidence bounds for PDF
I build confidence bounds for estimating PDF of the empirical sample using bootstrapping:
...
0
votes
1answer
50 views
Random sample using KDE or bootstrapping
I have an experimental sample, size of about 1000 values. I need to generate a much larger sample for simulation. I can create a samples like this:
...
0
votes
0answers
45 views
Estimate PDF from only positive data [duplicate]
I have experemental data that not contain negative values (and theoretically can not contain negative values). When I do kernel density estimates of probability distribution function in ...
0
votes
1answer
106 views
R: Density estimation vs Histogram Estimation
To draw random samples from a custom distribution, I recall reading that KDE's are better than histograms. (See hadley's comment here.)
When I experimented in R, I am finding that the KDE method ...
1
vote
0answers
11 views
Efficent global minimum search of costly to evaluate low dimensional error function
Trying to minimize the error from a probabilistic regression model which is composed of hierarchical KDE estimated PDFs. The top level is the result of 2-4 separate meta-PDFs from different data ...
2
votes
2answers
101 views
kernel density estimation of a Dirichlet distribution
I have a Dirichlet distribution over the parameters of a multinomial, and I want to estimate its posterior density given some set of evidence. Due to some pecularities of my problem (e.g. presence of ...
1
vote
1answer
59 views
Are Product KDEs proper density functions?
I have a fairly simple question about (multivariate) Kernel Density Estimators, but I somehow don't seem to find the answer anywhere: are these estimators supposed to specify a proper probability ...
1
vote
0answers
74 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
vote
1answer
142 views
simple sampling method for a Kernel Density Estimator
I have developed a simple Kernel Density Estimator in Java, based on a few dozen points (maybe up to one hundred or so) and a Gaussian kernel function. The implementation gives me the PDF and CDF of ...
2
votes
1answer
104 views
Density estimation with scaled sinc-like kernels
Given data points $x_i$ in $\mathbb{R}^d$ with function values $f_i$,
one can estimate the function at a given $x$ by
$\ \ \ \ \text{f}_{est}( x ) = \frac {\sum { w_i f_i }} {\sum { w_i }}$
with $w_i ...
4
votes
2answers
469 views
Advantage of kernel density estimation over parametric estimation
Is there any particular reason you will choose the kernel density estimation over the parametric estimation? I was learning to fit distribution to my data. This question came to me.
My data size is ...
3
votes
0answers
77 views
Proving the convergence of KDE algorithms when the samples are non-i.i.d
I am currently working on convergence proof for a new method for non-parametric importance sampling, and I need some help...
My method uses an MCMC algorithm to generate a set of dependent $M$ ...
2
votes
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.
...
3
votes
2answers
313 views
How to estimate the mode using non-parametric methods of a 4-variate random vector drawn from a continuous multivariate distribution?
I have a sample of size 10,000 of a 4-variate random vector coming from a (unknown) continuous multivariate distribution.
How can I estimate the mode of this density using nonparametric methods?
I ...
3
votes
1answer
151 views
Constrained kernel density estimation
Suppose you are trying to estimate the joint density $p(x,y)$ based on observed $(X,Y)$. However, you know that the marginal density $p(x)$ is uniform. How can you use this information to improve ...
2
votes
0answers
138 views
When/why should “rabbit ears hunting” density estimation “work”? [closed]
I came by Bernard Silverman concept of "rabbit ears hunting" interactive procedure for density estimation.
Does anybody know:
A recommended reference for this procedure?
An ...
6
votes
4answers
978 views
Which is the formula from Silverman to calculate the bandwidth in a kernel density estimation?
I want to calculate a better bandwidh for my kernel density estimator, which is an Epanechnikov. I use Silverman's formula which involves the standard deviation of the sample, the sample size and a ...
7
votes
4answers
280 views
How can I estimate the density of a zero-inflated parameter in R?
I have a data set with lots of zeros that looks like this:
set.seed(1)
x <- c(rlnorm(100),rep(0,50))
hist(x,probability=TRUE,breaks = 25)
I would like to draw ...
3
votes
0answers
188 views
Averaged continuous Kernel Density Estimates in lieu of a discrete Kernel Density Estimate in Monte Carlo Proceedure
I am thinking of using this code in a Monte Carlo routine to generate Kernel Density Estimates for subsequent use in a Naive Bayes Classifier (see this earlier post).
The author of the code states ...
4
votes
1answer
840 views
Use of kernel density estimate in Naive Bayes Classifier?
This question is a follow up to my earlier question here
and is also related, in intent, to this question.
On this wiki page probability density values from an assumed normal distribution for the ...
11
votes
1answer
883 views
Interpretation/use of kernel density
This may be a naive question, but here goes. If I have a set of empirical data and fit a kernel density to it, and then obtain a new single value which possibly comes from the same process which ...
12
votes
1answer
310 views
If variable kernel widths are often good for kernel regression, why are they generally not good for kernel density estimation?
This question is prompted by discussion elsewhere.
Variable kernels are often used in local regression. For example, loess is widely used and works well as a regression smoother, and is based on a ...
3
votes
2answers
792 views
How to use Kernel Density Estimation for Prediction?
I would like to apply KDE to inventory replenishment, but I am not sure how to use the analysis to predict future sales based on past sales. Given a set of data and having applied KDE to it (probably ...
7
votes
3answers
827 views
Adaptive kernel density estimators?
Can anyone report on their experience with an adaptive kernel density estimator?
(There are many synonyms:
adaptive | variable | variable-width, KDE | histogram | interpolator ...)
Variable kernel ...
7
votes
2answers
597 views
Kernel bandwidth in Kernel density estimation
I am doing some Kernel density estimation, with a weighted points set (ie., each sample has a weight which is not necessary one), in N dimensions. Also, these samples are just in a metric space (ie., ...
15
votes
2answers
684 views
Choosing a bandwidth for kernel density estimators
For univariate kernel density estimators (KDE), I use Silverman's rule for calculating $h$:
\begin{equation}
0.9 \min(sd, IQR/1.34)\times n^{-0.2}
\end{equation}
What are the standard rules for ...
