Questions tagged [empirical-cumulative-distr-fn]
Empirical cumulative distribution function: a step function increasing by $1/n$ at each unique $X$-value that occurred in the sample.
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Adjustment needed for multivariate Dvoretzky–Kiefer–Wolfowitz inequality on MCMC samples?
I was thinking about studying bounds on the multivariate empirical cumulative distribution function for samples from an MCMC chains. The multivariate Dvoretzky–Kiefer–Wolfowitz inequality would seem ...
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Covariance between two binomial random variables or expectation of product of binomial random variables
I have an empirical distribution $S_n(x)$ (= proportion of samples less than equal to x) from a random sample $X_1, X_2, ..., X_n$ for a random variable $X \sim F_X$. Consider the random variable $T_n(...
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Kolmogorov-Smirnov test with median ranks instead of traditional empirical distribution function
I know that Kolmogorov-Smirnov test uses the empirical distribution function of the sample studied $\widehat{F}(X_i) = \frac{i}{n}$ and then measures the adequacy of function $\widehat{F}$ to function ...
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Convergence of histograms
Good morning,
I'm interested in the convergence of empirical distribution functions. In particular, let us suppose to have a population with unknown distribution function $ F(x) $ and that I can ...
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Random variables implicit in Glivenko-Cantelli theorem
Theorem 1 here states $\lim_{n\to\infty} \sup_x |\mathbb{F}_n(x) - F(x)| = 0$ with probability $1$, where $\mathbb{F}_n$ is the empirical distribution function of the first $n$ $X$s, which are i.i.d. ...
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Compare CCDF of datasets with different sample size
I have 3 dataframes with the same structure (each dataframe includes a different type of tweet). Here are the columns of dataframes: id, ...
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Estimation of Distribution using multiple ECDFs
Every day, I keep track of the processing times for each input to my CPU and create empirical cumulative distribution functions (ECDFs) based on this data. Let's assume I have 100 observations per day ...
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Quantize a continuous random variable
Suppose we have a continuous random variable $X$. We do not know its distribution function, but have $n$ i.i.d. samples.
I am looking for methods that quantize (discretize) $X$ into a categorical ...
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How to apply a seasonal index into an eCDF?
I am doing a Before/After analysis, which is aiming at evaluating the effect of a change. Assume I have made a change at the beginning of June-2022, and I want to evaluate the effect of the change ...
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How to simulate critical values for a two sample KS test?
I believe I managed to simulate critical values for a single sample with the following code in R.
...
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Averaging ECDF vertically : Proof of convergence
Suppose we have a set A , we split into multiple disjoint subsets ai
We only have access to the ai sets , is there a way to compute the ECDF for the set A without looking at it ?
If for example we ...
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Doubt in empirical distribution, sample and random variable
I have a basic doubt in sample and random variables. I have read related posts on this site but still some doubt is still left.
Suppose we have a population and we are drawing some entries from it ...
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How to compare two empirical CDFs obtained from current status data
I have failure time information of a product in the form of current status data, which looks something like this.
Observed Time($t_i$)
failed ? ($y_i$)
5 hrs
1
6 hrs
0
5 hrs
0
7 hrs
1
...
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1
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How to plot a confidence band around the ecdf in the form (ecdf-b, ecdf+b) for a certain b in R?
I need to plot a confidence band around my ecdf. I calculated a value b and I basically just need to plot ecdf+b and ecdf-b but R doesnt let me do that. Does anyone know how this can be done?
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Empirical distribution function by sampling from a m.v. distribution
I have mathematically rewritten my problem as a function of multiple iid variables:
$$
f(X_1, X_2, ..., X_n),
$$
where $$X_i \in \mathcal{N}(0,1)$$
I now want to determine the empirical distribution ...
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When was the earliest appearance of Empirical Cumulative Distribution Plots?
I would be surprised if we actually had a date here. I am curious who, if anyone, created the ecdf plot. When did the ecdf make its first appearance? If we do not know when the first ecdf plot was ...
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Does this statistic comparing two samples using EDFs have a name?
So, I wanted to cook up a statistic that was similar to the Wasserstein metric for finite sized samples from distributions on a continuous support that is also invariant to reparameterization of the 1-...
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Substitution principle - Sample mean estimator
We can read in Keith Knight - Mathematical Statistics Example 4.22 the following.
EXAMPLE 4.22: Suppose that $\theta(F)=\int_{-\infty}^{\infty} h(x) d F(x)$. Substituting $\widehat{F}$ for $F$, we get
...
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How to estimate correlations between the standard errors on empirical quantile estimates from two correlated series?
I have two very long series of samples from a pair of correlated random variables, $x$ and $y$.
(they are fat tailed distributions, roughly Pareto, if that helps).
(if you're curious...$x$ and $y$ are ...
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Is there an equivalent to an ECDF with a "<" sign?
The empirical cumulative distribution function is defined as
$$
F(x)=\frac{1}{n} \sum_{i=1}^{n} \mathbb I_{x_i \leq x}
$$
Is there an equivalent interpretation of this function as:
$$
F(x)=\frac{1}{n} ...
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Covariance of the empirical probability mass function
Suppose a discrete random variable $Y$ takes $k$ levels of different values $y_1,y_2,...,y_k$. Let $P(Y=y_k):=p_k$.
Suppose we have $n$ i.i.d. samples of $Y$, my question is:
How can we compute the ...
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Simulating New Observations from a Empirical Distribution Function [duplicate]
Recently, I found out that it is possible to simulate new observations from an Empirical Distribution Function (EDF) . Suppose I collected some data (e.g. heights of baksetball players): $x_1, x_2,\...
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Empirical distribution for feature binning
In paper "A simple yet effective baseline for non-attributed graph classification" (https://arxiv.org/pdf/1811.03508.pdf) authors use empirical distribution for feature binning. Precisely, ...
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symmetrization in glivenko-cantelli proof
In this proof of the Glivenko-Cantelli theorem, page 2 of these notes, two types of symmetrization are used. The first transforms the sup of the centered empirical cdf $$P(\sup_{z\in\mathbb{R}}|(1/n)\...
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rank to quantile estimate?
R> x=c(92, 3, 1, 4, 15, 4)
R> rank(x)
[1] 6.0 2.0 1.0 3.5 5.0 3.5
Given the rank results of an input vector of sampled data, I can estimate the quantiles ...
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How to average several posteriors distributions from a Monte Carlo Simulation
Say you produce several posteriors distributions from different runs of the same model under different seeds. That is to say you have something like the following:
...
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Distance between two categorical distributions
I want to test whether two (empirical) categorical distributions taking on $K$ possible values (e.g. 5, with no innate underlying ordering) with associated (empirical) probabilities $p_k$ are the same....
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Probabilities from ecdf and normalised data - is this acceptable difference? (using R)
I have a dataset of 500 observations, definitely not normally distributed: The minimum is around 50 000 in the data. If I use ecdf to predict e.g. 100 000, I get:
...
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What is a "bootstrap from the Empirical CDF"?
I am given the following set of questions.
In all the questions the basic background is a sample X_1, ... , X_n.
If n=3 and you observe the numbers 1, 2, and 4, what is the sample median.
If you ...
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Determine a product's rating given a known 3-sigma tolerance
Consider a 100Ω resistor with a 10% tolerance. We can assume this is the 3-sigma value since this is typical in manufacturing. Thus, we can expect ≈99.73% of such resistors to range from 90Ω to 110Ω.
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Smoothed CDF to calculate asymptotic normality
If we have the following estimator: $\hat{F_Z}(z)=\frac{1}{N}\sum_{i=1}^N1\{Z_i\leq z\}$. The CDF of $Z$ is defined as $F_Z(z)=Pr(Z\leq z)$. $Z_1, ..., Z_N$ is i.i.d. data.
What would be the steps to ...
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Plug-in principle with kernel density estimate
The plug-in principle says that to estimate a statistical functional of the form
$$
T(\mu) = \int f(x)\ d\mu(x)
$$
we can replace $\mu$ with the empirical distribution $\mu_n$ depending on data $X_1,\...
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What are the simplest examples of nonlinear statistical functionals?
I am reading Wasserman's book "All of Statistics" in which he defines a statistical functional as any function $T(F)$ of the cumulative distribution function $F(x)$ that outputs a real ...
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Picking a specific estimated CDF from a set of CDFs provided by an ECDF
Let $F_X$ be a CDF of an unknown random variable $X$. If we have independent samples $x_1, x_2, \ldots, x_n$ of $X$ then we can estimate $F_X$ non-parametrically using an ECDF $\hat{F}_n$. By Central ...
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Notation for ECDF
I'm reviewing statistical functionals and U-statistics, trying to make notes, and I am tripping on notation. From my understanding, $X$ is used to denote a random variable and $x$ is used to denote an ...
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Density estimation from ECDF - numerical derivatives and scaled domains
Suppose we want to get a density estimate of some data X. One way is to compute the empirical CDF,
...
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Nonparameteric Empirical Estimator For Stochastic Process
Motivation: If $X$ is a random-variable defined on some probability space $(\Omega,\Sigma,\mathbb{P})$ then Glivenko-Cantelli lemma guarantees that the empirical distribution $\frac1{N}\sum_{n=1}^N \...
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How to deal with a PDF that takes CDF data as input? (Metalog distributions)
I am trying to utilise the Metalog distribution in a Machine Learning project.
For this project, I need to be able to obtain likelihoods using the PDF of the distribution.
https://en.wikipedia.org/...
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Is it possible to convert a pdf obtained with density() to an ecdf?
Consider the following code that gives us (an estimate of) the pdf of a random variable $X$:
X = rnorm(100,10,1)
XDensity = density(X)
I want to obtain the ecdf of ...
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Fitting a function to ECDF [closed]
I have some ECDFs. I would like to summaries the ECDFs with functional approximations. I was thinking that a polynomial, spline, or other line fitting procedure would generate a nice parsimonious ...
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Computing the most representative sample of a random variable
Let $X$ be a real-valued random variable and $n > 0$. Using numerical methods, how can we find the vector $\vec v$ of $n$ real numbers that is most characteristic of $X$, in the sense that the ...
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Median from two unmergable datasets
I am trying to calculate the "overall" median of a variable that is spread across two datasets. I have access to the raw data in each dataset but can't bring their raw data together. What ...
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Why are people using KS statistic to choose cutoff for binary predictions?
I've seen that people are sometimes using Kolmogorov-Smirnov statistic to determine a cutoff in a binary classification models, such as logistic regression. However, i do not fully understand why and ...
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Obtaining an expression for empirical mean from empirical CDF definition
This is my first post so I will try to be as clear and concise as possible. I am doing a course in statistics and we define the true mean of a random gaussian variable to be as follows:
$\mu$ = $\int_{...
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Confidence Interval of p-Quantile from Empirical CDF
I am trying to provide an interval estimate for the 0.8-quantile of some numeric data, which is assumed to be an IID sample from some unknown, continuous distribution. I constructed an Empirical CDF ...
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Empirical estimation of conditional distribution $Y|X$ at the boundaries of X
I want to estimate conditional distributions of Y | X. Where X contains several continuous covariates.
I'm coding in R. I tried several methods so far, but none gives me entirely satisfactory results ...
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Does the Glivenko-Cantelli theorem work back and forth?
If we have a sample $X_1, X_2, \ldots, X_n \sim F$ then $\hspace{1mm}sup_x|F_n(x) -F(x)|\xrightarrow{a.s./p}0$.
Now, if I can come up with a theoretical cdf $F$ such that $\hspace{1mm}sup_x|F_n(x) -F(...
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Is it accurate to take the maximum distance between CDF and ECDF only at the edges? (Kolmogorov-Smirnov Test)
I have two samples, one obtained empirically and the other is the result of a simulation. I want to tune the simulation so that the result resembles the reality, for that I will minimize the KS ...
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Example of cumulative distribution function and the empirical distribution function [closed]
A random of 100 rolls of the die. The outcomes 1, 2, 3, 4, 5, 6, occurred 13, 19, 10, 17, 14, 27 times, respectively. Calculate the cumulative distribution function and the empirical distribution ...
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