Questions tagged [continuous-data]

A random variable $X$ is called continuous if its set of possible values is uncountable, and the chance that it takes any particular value is zero ($\text{P}(X = x) = 0$ for every real number $x$). A random variable is continuous if and only if its cumulative probability distribution function is a continuous function.

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How to treat low frequency continuous variable in machine leanring

Hello I am working on machine learning model for count data, and I have various features that are highly skewed. The frequency table for one of the feature is given below. ...
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Specification on power spectral density for population data

What is the best way to put a specification on the single-side auto-power spectral density (PSD)? We have a product for which we have a time signal. For this signal we calculate the PSD to determine ...
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Estimating a logistic regression with OLS? [duplicate]

NB: This question is different from this one which assumes that we have computed the LHS of the regression equation with no issue. My question is about how to compute this LHS. Consider a simple ...
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9 views

Rate of convergence after applying a non-random, continuous function [migrated]

Assume that $\theta_n$ is $O(n^d)$ meaning that as $n\rightarrow \infty$, then $\theta_n / n^d$ is finite. Suppose you have a some non-random and continuous function $f$. Now, my question is, what ...
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Determining best cutoff value between 2 groups

I have 2 groups (say G1 and G2) and there is a continuous variable (say V) which is significantly different between 2 groups. The mean values for 2 groups are m1 and m2. I want to determine what is ...
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Why probabilities are measured over intervals (instead of points) for continuous probability distributions? [duplicate]

In case of discrete probability distributions, we find probabilities of different points/values over exactly those points, but in case of continuous probability distributions, we find probabilities of ...
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32 views

Does treating trial number as a continuous variable for linear models lose information?

If I create a linear model where Trial number is one of the predictors, am I losing any information by treating it as continuous (when in fact it is actually discrete + ordinal)? I believe the answer ...
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1answer
11 views

diff-in-diff with continuous treatment vs cut-off point (and 2 periods)

I have panel data for a number of counties for two years only (t = 1,2). The treatment, let's assume it's a policy implementation programme, happens in between year 1 and 2 and it is a continuous ...
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What is the best test to compare two sets of continuous data to see if they differ at each pair and overall?

I have two sets of data comparing the mean consumption of 4 materials before and after a new production standard have been introduced. i have 4 different materials and the consumption of each one is ...
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40 views

analysis of variance on zero inflated semi continuous data

I have a fairly fundamental problem with my data, they do not suggest that they were sampled from a normal distribution. This is problematic because I would like to run some sort of analysis of ...
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Discrete weather data causing issue in predicting spatial yield

I want to incorporate the weather data into predicting spatial yield. Major data used: 4 years spatial yield data from a 10-ha crop field 4 years of daily weather data such as rainfall from a ...
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X given Y is a normal random variable with mean Y and var 4. Y~Binomial(10, 0.6). Find E(X) and Var(X)

Is this a joint probability distribution? Can I assume X and Y are independent and that E(XY)=Y? I am stuck at this question.
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Machine learning on Percent as dependent variable [closed]

I am working on a problem where I am tasked to predict users into 'High users' and 'Low users'. Dependent variable provided is in percent of orders (%) which is calculated as (#orders placed/#sales ...
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Normal V Binomial Distribution (Elementary Question)

CONTEXT: First year university statistics course exam question Suppose couples decide to have children until they either have a child of each sex, or they have three children. Assume that births are ...
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How to treat missing values in a continuous variable?

I have a classification problem and I am doing the data analysis. I came across a variable which is numeric continuous and have some missing values. I checked the missing values and are real missing ...
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58 views

Best approach for dealing with continuous predictors with missing data in random forests

I was thinking about a problem I'm facing: I have wage data that I want to add to my model, but it's incomplete (data for about 70% of my observations). So, I was ...
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Is there a Continuous Conditional Variational Autoencoder?

Conditional Variational Autoencoders (CVAE) are an extension of Variational Autoencoder (VAE). In VAEs we have no control on the data generation process, something problematic if we want to generate ...
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Continuity of the joint CDF

Consider two random variables $(X,Y)$. Suppose that the joint cumulative distribution function (CDF) of $(X,Y)$ is continuous. Is it true that this implies: 1) The CDF of $X$ is continuous? 2) The ...
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How to make SalePrice as a discrete value?

The target variable, Saleprice originally is a continuous value. I calculated ...
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how entropy works in continuous distribution using the K nearest neighbour?

So calculating entropy in continuos distribution was developed by Kozachenko and Leonenko (1987). I have seen one implementation here. however I have problem understanding the approach. basically, ...
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63 views

How to model a zero-inflated 'continuous' response data in r 'without' assuming an underlying normal distribution?

I have a weather data set with rainfall as response. It has 56% observations as 0, while the rest as continuous rainfall data. I can't use tobit, hurdle or any other zeroinfl() model as they require ...
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Is there a standard name for this continuous distribution? [closed]

I'm encountering the following PDF of continuous scalar real $X$ with semi-infinite support $]0,+\infty[$: $$ f_X(x) = C ~ x^{-\alpha} ~_1F_1\left ( a,b;-\frac{d}{x^\beta} \right ),~~~~~~\beta>0;~\...
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Repeated measures design (2x2 within subject variables) with one continuous variable and one covariate

I have a repeated measures design (one DV measured twice, both times in two different conditions) with one continuous IV and one (continuous) covariate. I'm trying to see if there is an interaction ...
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Estimate total effect of treatment when continuous treatment variable and its dichotomization show contradicting impacts

I am trying to estimate the total effect of a leak of a harmful gas on my dependent variable. While I do not know the actual exposure, I know the distance of a household to the origin of the leak in ...
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Why is the Cauchy Distribution so useful?

Could anyone give me some practical examples of the Cauchy Distribution? What makes it so popular?
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Conditional survival probability up to time $T$ given $t > s$

This is a really basic question I know but for some reason I'm failing to convince myself of the right answer here. Given a survival model that has CDF $F(t) = \mathbb{P}(\text{failure before}\ t)$ ...
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1answer
21 views

Explanation of multiple linear regression output

Just looking for some help with the interpretation of my multiple linear model output and also some validation on the methods I used. I have 1 response - Ball speed and 9 continuous predictors and 1 ...
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is there a difference between E[e|x]=0 and E[e|d=1]-E[e|d=0] in continuous vs discrete case in regressions?

in the discrete case, if assignment is random, then i can express E[y|d=1]-E[y|d=0] = B + E[e|d=1]-E[e|d=0], where the expectation of the errors are the same for both groups and become zero. Where I ...
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How to choose sample size from probability density for computing mutual information based on continuous variables

I need to compute mutual information gain based two continuous variables $X$ and $Y$ $I(X|Y) = \int_X\int_Y p_{x.y}(x,y) \log(\frac{p_{x.y}(x,y)}{p_{x}(x)p_{y}(y)})$. I have used Kernel Density ...
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Conditional probability formula for continuous random variables [duplicate]

Let $V,T$ be two random variables with supports $\mathcal{V},\mathcal{T}$, respectively. Let $P_{V|T}$ denote the probability distribution of $V$ coditional on $T$ $P_{V,T}$ denote the probability ...
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34 views

Application of law of total probability for continuous random variables

Consider 3 random variables $Y,V,T$, with supports $\mathcal{Y},\mathcal{V},\mathcal{T}$, respectively. Let $P_{Y,V}$ denote the probability distribution of $(Y,V)$ $P_{V}$ denote the probability ...
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Are multicollinearity an issue for continuous variables only, and maybe ordinal variables, but not for nominal variables?

To avoid multicollinearity, correlation analysis can be conducted between variables. Some applicable tests for correlation measurement are Pearson's correlation. Spearman's rank correlation. ...
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Clustering Binary and Continuous Features

If you need to cluster a dataset with the following characteristics: It has a mix of binary and continuous features. It is very sparse. For most features, you only have values for 15% of the ...
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What does “density” really mean in probability density function in statistics? [duplicate]

I am familiar with the concept, but I simply can’t get my head over the intuition behind it. While being a derivative, it describes the rate of change for one unit. Simply put, we can say that it ...
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1answer
39 views

Correlation between discrete and continuous data

I would like to caculate the correlation between two vectors. One vector represents the intensity of an emotion as continuous data between 0 and 100. The other vector represents the intensitiy of an ...
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1answer
32 views

Can a discrete random variable be absolutely continuous of a continuous random variable?

I have a question in measure theory: given two measures $\nu$ and $\mu$, we say that $\nu$ is absolutely continuous of $\mu$ if for Borel set $A$ such that $\mu(A)=0$, we have $\nu(A)=0$. I want to ...
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1answer
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Using one-hot encoded features along with continuous-valued features?

The task I wanted to do is a prediction task where most of the features are continuous numbers and some of the features are one-hot encoded. I am training a neural network and I wondered that, is it ...
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1answer
29 views

What type of graphic will be suitable for 3 continuous vs 1 categorical variable [closed]

I want to create a graphic to explore relationship between 3 continuous and 1 categorical variable. I have 2 different examples I want to investigate. 1- the numeric variables are num of bedrooms ...
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Preference test with two continuous variables

I have two continuous variables, the height of a woman and the height of her male partner. With this data I would like to test preferences, e.g. whether there is a preference for shorter women to be ...
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How to compute and interpret interaction term between continuous variables that have negative values?

I was wondering about the computation and interpretation of interaction terms of continuous variables that are used in a multiple regression. Normally, one would mean-center (or z-standardize) the two ...
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18 views

How to model zero-inflated mass data? [duplicate]

I am working with a data set of the mass of plastic found at various sites. At most sites, we found no plastic and so the data is zero-inflated (see histogram below). I want to model the data using ...
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are there any supervised learning methods that can only be applied to data with continuous features?

Are there any methods that exclusively work on continuous features? At first i imagined that linear models would demand this, but discrete values can be transformed and encoded such that they can be ...
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How to transform Beta distribution of second kind into Beta distribution of first kind?

! I tried to solve it in a way but I can't complete in as I don't get any idea how to integrate the Beta kind two integrand within a finite boundary.
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Get posterior distribution of categorical variable given empirical continuous-categorical priors?

Suppose I have categorical variable $Z \in D$ defined for some finite domain $D$. I also have a continuous variable $X \in \mathbb{R}$ which is observed. From historical data samples I have the ...
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What to use as a “stop” signal in a hierarchical LSTM model with continuous variable-length outputs

I am implementing a hierarchical sequence-to-sequence deep neural network model using long short-term memory (LSTM), where the bottom level of the hierarchy generates discrete outputs (characters from ...
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Balancing continuous covariates for oversampling

I'm currently looking into methods for restoring balance of a biased dataset with respect to a continuous variable. My problem is similar to this question, with the slight difference that I'm dealing ...
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28 views

Statistical test for three independent variables and one dependent variable

I use SPSS for my data analysis and cannot seem to think of a proper test to test my hypothesis. I have three independent variables (1. direct emotion expression, 2. indirect emotion expression and 3. ...
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2answers
87 views

Ranked-categorical variables in Artificial Neural Networks?

As far as I understand, input variables in Artificial Neural Networks (ANN) must be continuous, right? (And there are a number of methods to convert categorical to continuous variables described in ...
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1answer
64 views

Paired vs. Pooled Inference… When is it okay to pair samples?

I understand that paired tests are usually done on sampling distributions that have some sort of linkage. But is there a definitive way to differentiate when to use a paired t test vs. pooled? The ...
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Continuous measure from one specimen. Is there an effect of our intervention? [R]

We have a case report, where an intervention could influence the parameters. We have continuous data (1 Hz) and intervention happens two times during the measurement period. I have depicted the data ...