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conditional density plot in R base. Error in cdplot.formula(date ~ presence_absence, data = df_long) : dependent variable should be a factor [closed]

df <- read.csv("data_for_gs.csv",header = TRUE) df_long <- pivot_longer( df, cols = starts_with("Grinding") | starts_with("Pounding"), names_to = "activity&...
user24598620's user avatar
0 votes
0 answers
62 views

Measuring the Distance Between KDE Distributions with Different Bin Counts

I have two KDE distributions, each with a different number of bins. I'd like to compare them effectively, and I'm wondering if there's a recommended technique for this. Should I unify the number of ...
Adham Enaya's user avatar
0 votes
0 answers
29 views

Kernel Density Estimation, Bandwidth Tuning, Independence, and Comparison

like many of us here, I turn to kernel density estimation when I need a nonparametric estimate of a numerical feature's distribution, and in an attempt to assume as little as possible, I usually use ...
user3163829's user avatar
2 votes
0 answers
38 views

Fast measure of "clusteredness" of points?

I have a cloud of points in a bounded volume in 2D (lets say 2d for now, though it'd be nice to generalize to any dimension): $<p_n \in \mathbb [0, 1]^2: n \in [1..N]>$ I'm looking for some ...
Peter's user avatar
  • 614
2 votes
1 answer
79 views

Parametric vs non-parametric generative models

I have a little perplexity trying to distinguish parametric vs non-parametric generative model. In my understanding, a parametric generative model would try to learn the probability density function ...
James Arten's user avatar
0 votes
0 answers
50 views

How to prove symmetry of a Uniform kernel?

I am trying to prove this kernel is valid, $$ K(x) = \frac{1}{2}I(-1 < x < 1) $$ So far I can integrate to 1, but how do I prove $$k(x) = k(-x)$$ Also, how do we satisfy that k(x) is $\ge$ 0 for ...
user359211's user avatar
0 votes
0 answers
189 views

density function estimation in r

I firstly generate data from a bivariate normal distribution. Here comes the code. ...
Aurora Joy's user avatar
0 votes
1 answer
2k views

Dealing with bimodal residuals

I want to run linear models to understand the effect of single predictors on an outcome. This is quite straightforward, but I am facing a situation where my residuals appear to be bimodal. I can't ...
Rnovice's user avatar
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1 vote
0 answers
86 views

Nonparametric Methods for Two Sample Quantile Test [duplicate]

Background I want to compare two populations' quantile, e.g. 99.99% quantile, 95% quantile. So I am searching for methods for two sample quantile testing. Unfortunately, the population does not obey ...
Travis's user avatar
  • 227
1 vote
0 answers
99 views

Optimal rate of convergence of nonparametric density estimators

Suppose that $X_1, X_2, \dots, X_n$ forms an independent and identically distributed sample from some $d$-dimensional probability distribution with unknown probability density function $f$. Let $x$ be ...
lmaosome's user avatar
  • 140
6 votes
2 answers
1k views

How to write a joint kernel density of two random variables with known individual densities?

Consider two random variables $X$ and $Y$ with densities $${f}_1(x) = \frac{1}{n_1h_1} \sum\limits_{i=1}^{n_1}K\left(\frac{x-u_i}{h_1}\right) ~~~~\text{and} ~~~~ {f}_2(y) = \frac{1}{n_2h_2} \sum\...
Shanks's user avatar
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1 vote
0 answers
38 views

Driver based forecasting using past distributions

I have reduced my original forecasting problem (Short context : I need to forecast hotel bookings and checkins for the next 3 months. I already have a reasonable forecast for bookings and need to ...
Roopanjali Jasrotia's user avatar
2 votes
2 answers
125 views

what are the Kernels with zero variance (in KDE)?

I am studying about Kernels in Kernel Density Estimation and I came to understand that the bigger the $n$ that satisfies $\int_{-\infty}^{\infty}K(u)\cdot u^{j}du=0$ for all $1\leq j \leq n$ the more ...
Lazag's user avatar
  • 63
2 votes
1 answer
112 views

Density plot with epanechnikov with exceedance data

I'm trying to replicate empirical density plot from the paper "Computing Maximum Likelihood Estimates for the Generalized Pareto Distribution". The data is ...
forecaster's user avatar
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0 votes
0 answers
200 views

Computation of the density of the ratio of two random variables

Background: For two continuous random variables, $X$ and $Y$, the density of $Z := \frac{X}{Y}$ is given by \begin{equation} p_Z(z) = \int_{-\infty}^\infty \lvert y\rvert\, p_{XY}(zy, y) \, \text{...
R. Rayl's user avatar
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0 votes
1 answer
2k views

Is it necessary to normalize the dataset before kernel density estimation?

Is it necessary to normalize (Z-score) the dataset (high dimension) when the dimensionality of features varies greatly? If I normalize the dataset, then the probability density (f1) obtained by KDE ...
Gid's user avatar
  • 86
2 votes
0 answers
100 views

Predictive density via LOOCV

I am looking for a way to generate a density prediction (in contrast to a point prediction or a prediction interval) in a multiple regression setting without relying on stringent parametric ...
Richard Hardy's user avatar
3 votes
1 answer
98 views

how to understand this math formula for bandwidth calculation?

I am reading a paper that uses the following equation to calculate the optimal bandwidth, however, I am confused about the position of "4" and "3" in the equation. is this a typo? or what does it mean?...
flashing sweep's user avatar
7 votes
1 answer
909 views

Kernel density estimate vs Dirichlet process mixture

Nowadays the Dirichlet process mixture (DPM) seems to be the default Bayesian approach for density estimation. My question is why not simply use the kernel density estimate (KDE) to model the density? ...
Frank's user avatar
  • 71
0 votes
0 answers
78 views

Behavior of kernel density estimation

Consider the random variable $X=YZ$, where $Y\sim\text{Normal}(0,1)$ and $Z\sim\text{log-Normal}(0,1)$ are independent. I wanted to assess the accuracy of kernel density estimates for the density ...
user260042's user avatar
6 votes
1 answer
198 views

Density estimation as an optimization problem

Density estimation is the estimation of a probability density function from observed data. Can some of the common approaches to density estimation, such as kernel density estimation, be formulated as ...
user76284's user avatar
  • 993
0 votes
0 answers
50 views

Does the limit of c exist or not

Let say m is dimension $\exists$ $f(x)$ $f$ is density function and \begin{equation*} f(x) = \frac{c(m,a,b)}{\|x\|^a\left(\log\frac{e}{\|x\|}\right)^{b}}\mathbf{1}_{\|x\|\leq 1} \geq 0 \end{...
mhmt's user avatar
  • 53
4 votes
2 answers
378 views

how to know this integral finite or infinite

In here, i want to show this entropy exist or not exist, namely i should calculate the integral of $\int_0^c\frac{1}{x\log^2\frac{e}{x}}\frac{1}{2} \log\frac{e}{x}\,dx$. If the result is $ <\...
mhmt's user avatar
  • 53
2 votes
4 answers
4k views

Bandwidth parameters in multivariate KDE using scipy.stats.gaussian_kde

I am working on a project which involves implementing in Python two different density estimation functions over multivariate data; one using N-d histograms and the other using kernel density ...
Zac's user avatar
  • 270
0 votes
0 answers
31 views

Best way to conduct and present simulation results of nonparametric density estimation

I am conducting a procedure where I first estimate some auxiliary functions (nonparametrically using kernels) and then, knowing their relations to the densities of interest, I use these auxiliary ...
Alik's user avatar
  • 578
3 votes
1 answer
357 views

Kernel density estimation with FFT for a univariate non-parametric regression

The non-parametric regression model to be estimated looks like the following x_t = b(x_t-1) + epsilon_t Forfinding the optimal bandwith h in the kernel ...
InDubio's user avatar
  • 41
4 votes
0 answers
127 views

Distribution (CDF) estimation for strictly increasing, continuous distribution with compact support

For all $t\in 1,\dots,T$, suppose $x_t\in [0,1]$ is a draw from a distribution with unknown CDF $F:[0,1]\rightarrow [0,1]$. For future use, define $\tilde{x}\in [0,1]^T$ to be a vector containing $x_1,...
cfp's user avatar
  • 535
1 vote
0 answers
17 views

Intensity deconvolution

We have a set of intensities (measured) $I_{j} = \cos(\theta_{j}) + N_j$ where $\theta_{j}$ is distributed according to some distribution between 0 and 180 degrees (well, in reality between 0 and ...
Ilya's user avatar
  • 121
4 votes
2 answers
4k views

Leave one out cross validation in kernel density estimation

I am taking a look at : http://pages.cs.wisc.edu/~jerryzhu/cs731/kde.pdf Where they define the following loss function for kernel density estimates $$J(h) = \int \hat{f_n}^2(x)dx -2\int\hat{f_n}(x)...
user2879934's user avatar
1 vote
0 answers
169 views

Generalized linear mixed model with time lags

I'm attempting to understand how random variables and one fixed variable are influencing an animal population over time. The trouble I am having is that my population density estimates were only ...
Krystal R.'s user avatar
2 votes
0 answers
54 views

Which distance and generative model?

I am wondering what is a suitable measure to separate the blue, green and red points? I tried 'cosine' but some of the red gets confused with green. My goal is to make two generative models; one ...
Leila's user avatar
  • 965
9 votes
2 answers
6k views

Density estimation for large dataset

I have a unidimensional data set with more than 1000000 observations. Assuming that those observations are independent realizations of the same random variable I need to estimate the underling ...
Mur1lo's user avatar
  • 1,375
6 votes
1 answer
1k views

Is there a difference between 1D Mean Shift and KDE for clustering 1 d data?

I need to cluster (or group) large one dimensional data sets into a set of fixed bins. I started out using K-means, but I want to look into other approaches. Two that I have found are Mean Shift and ...
Skander H.'s user avatar
4 votes
0 answers
88 views

Nonparametric estimation of the logarithm of a density

I was wondering whether there is an equivalent to Kernel Density Estimation to estimate nonparametrically the logarithm of a density. Or if there is any nonparametric method for that. (Taking the ...
epsilone's user avatar
  • 786
5 votes
2 answers
231 views

Nonparametric Identification from Order Statistics

Suppose a vector of random variables $(X_1,...,X_n,Y_1,...,Y_m)$ is such that $X\sim F(\cdot)$ and $Y\sim G(\cdot)$. So $X$ are distributed independently and identically as $F(\cdot)$ and $Y$ as $G(\...
419's user avatar
  • 51
3 votes
1 answer
1k views

What statistic does R's sm use to test equality of densities?

I'd like to ask what kind of test statistic is used in the R package 'sm' to test for equality of two density distributions. This is the package: https://cran.r-project.org/web/packages/sm/index.html ...
anymous.asker's user avatar
2 votes
1 answer
1k views

Understanding "the kernel has zero mean"

I am trying to understand kernel density estimation and found the graphic below illustrating different kernel functions on Wikipedia. I have no trouble reconciling it with the two statements "the ...
Fredrik P's user avatar
  • 482
1 vote
1 answer
108 views

Density Function Estimation

Given a sample of $n$ observations, which are assumed to be $i.i.d.$ and generated from a continuous probability law. Consider the question of estimating the density function $f(x)$. There are two ...
LaTeXFan's user avatar
  • 1,306
-1 votes
1 answer
248 views

question about a Rosenthal inequality

What is the usefulness of Rosenthal inequalities in (kernel) density estimation where $\xi _i .... \xi _n$ are independent random variables, $\mathbb{E}\xi_{i} =0$ and $c(p)=15p/lnp$ for $p>2$ ...
user44677's user avatar
  • 307
6 votes
2 answers
8k views

How to get percentiles from empirical density in R?

The density() function in R allows me to enter observations and get an empirical density that I can plot x and y values. I like ...
user52291's user avatar
3 votes
1 answer
3k views

R scatterplot matrix with nonparametric density

I normally use MATLAB, or JMP but right now am working with R. I have ~150 dimensional data with a few hundred thousand rows. Some of the columns are non-informative, they only have one value. This ...
EngrStudent's user avatar
  • 9,490