Estimation of probability density functions, whether by kernel density estimation, log-spline estimation or other methods.
Wikipedia has an article https://en.wikipedia.org/wiki/Density_estimation with further references.
Questions tagged [density-estimation]
332 questions
5
votes
3 answers
Parametric models for mixed discrete/continuous data
I'm curious if there are any common parametric distribution models for mixed discrete/continuous data. For illustration, suppose I have two random vectors, $X_c,X_d$, where $X_c$ is continuous and $X_d$ is discrete. I have data consisting of…
icurays1
- 502
4
votes
1 answer
Appropriate Kernel for Kernel density estimation
Given $\mathcal G$ an arbitrary family of density distributions, and a large number of samples from an unknown distribution $P$, is there any method to find the most appropriate kernel in $\mathcal G$ and then use it to estimate $P$ ?
Cauchy
- 93
3
votes
2 answers
How to learn a continous distribution incrementally?
Suppose i have a random process that generates a singe number $x \in [0, 1]$ per time step $t$. Let's call the process $\pi$. At the beginning i assume that the the outcome is uniformly distributed. Now as i receive $x_t$ i update my belief over the…
hh32
- 1,421
1
vote
0 answers
Density Estimation of High-dimensional Data
I would like to estimate the probability density function of a data set with a very large number of samples (50,000+) and a large number of continuous variables (2,048).
Compute efficiency is somewhat important, so I would like to avoid approaches…
1
vote
1 answer
DBSCAN vs Kernel Density Estimation
What is the difference between DBSCAN and Kernel Density Estimation
1
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0 answers
Truncated Non-Parametric Density Estimation
I have cross-sectional data for individuals, $i$, and years, $t$. I need to estimate the density of a random variable, $X_{it}$, which is equal to
$X_{it} = A_{it}/B_t$.
My data is truncated in the sense that I only observe $X_{it}$ if $X_{it} >=…
user9599717
- 11
1
vote
0 answers
Non-parametric density estimation: Is it better to estimate a PDF by first finding its CDF?
By better I mean fewer density error against the true PDF. Say that $X$ is the random variable that we wish to find its true PDF $f_X$ by the estimation $\hat f_X$. Then my goal is to find $\hat f_X$ that minimizes:
$$
\mathbb{E}[(\hat f_X(X) -…
caveman
- 2,701
- 2
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0
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0 answers
Density estimation for 50 input variables - individually or together
I am a beginner to statistics and was wondering if it is possible to estimate the density where there are 50 input variables.
Do I perform density estimation for each variable individually then multiply them or do I consider the density of a vector…
Gabi23
- 143
0
votes
1 answer
Using Parzen Window approach
When is preferable using Parzen Windows approach, so a nonparametric approach, instead to a parametric approach?
FbaStack
- 1
0
votes
0 answers
Distribution estimation
What would be a good strategy for estimating the joint distribution of a bunch of measurements?
So if I had drawn from a 2D Gaussian I would have given vectors:
[[ 3.30598028 4.42541811]
[ 2.53505053 1.29456389]
[ 0.66794753 -0.36475196]
…
Elias
- 109