Questions tagged [univariate]
Pertaining to a single variable. Univariate statistics deal with only one variable - e.g. the mean, standard deviation, range etc. Univariate distributions involve only one variable e.g. the univariate normal, uniform etc. distributions.
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Run univariate analyses meta-regression when have Infinitive value
Dear all,
I am an R beginner. I am trying to do a small project, and I am having trouble, so I want to get help from you.
My dataset includes p_male, the proportion of males; p_one-year, the ...
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LSTM Limitations for Time Series Forecasting
I am working on a project where I generate synthetic data which is the sum of 5 random sine functions sampled every 0.01s (and I add mean reverting brownian motion noise to the data).
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Bonferroni correction necessary? regression to predict ONE outcome
I am working on a project with a small sample size where I have multiple predictors at baseline and one IV. I am trying to see if any of the DVs are good predictors for the score on the IV (continuous)...
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Why can't fit I fit a multivariate regression (OPLS) model when my variables are univariately (Pearson/Spearman) correlated?
I have a dataset of 950 lipids (X) and want to see if any are correlated with cognitive function (Y). When I try to fit an opls regression model, it errors and says that "No model was built ...
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Potential evaluation based on the coherence of predicted value with actual data
I have the following data over time:
that means data collected for a single variable like CPU usage in lowest, highest, and average mode over time every 5 mins (data granularity = 5mins) like the ...
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Getting an equivalent of R-squared for simple univariate regression done with structural equation modeling
I need to calculate a very simple regression model outcome ~ predictor. To treat missings, I have to use FIML and also I need bootstrapping. Since the ...
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Weighted summing time associated data for univariate model?
I have a situation where I have several mice A, B, C, etc., each of which has several thousand datapoints, all associated with a timepoint of 1 year, in addition to a single datapoint associated with ...
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Evaluate upper bound prediction results using classic error calculation instead of PI metrics
I have the following data over time:
that means data collected for a single variable like CPU usage in lowest, highest, and average mode over time every 5 mins (data granularity = 5mins) like the ...
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Beginner - Univariate Analysis
I am trying to perform an analysis based on a survey response 'yes' vs 'no'. The response has multiple categories. For example, I am looking at a variable 'mechanism of injury' that has 4 categories, ...
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Multiple Univariate regression (lm) using a for loop but problem with missing values. (Using R studio) [closed]
I want to execute this code that work well when there is no missing values :
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Anomaly Detection in Multivariate and Univariate timeseries
I just started exploring Anomaly detection in timeseries for Univariate, Multivariate timeseries.
I read few articles about it, few research papers as well. But every article/research paper has ...
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Univariate vs. Multivariate Standardization
There are several common methods for scaling input features to machine learning models prior to training the model. The most popular methods seem to be standardization (centering by the mean and ...
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How to classify univariate time series data in real-time? [closed]
I have a robotic arm doing three different tasks, each task is colored differently as seen below. Data correspond to a Z-axis of the gyroscope sensor.
The idea is to detect anomalies. However, I need ...
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How to implement Girardi & Ergun's (2013) three-step multivariate GARCH estimation of CoVaR in R?
I'm trying to calculate multivariate GARCH estimation of conditional value-at-risk, by adopting a three-step model from Girardi & Ergun (2013) paper entitled "Systemic risk measurement: ...
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what R function can be used to fit an additional MA term at lag 3 to my ARIMA model?
I want to fit an ARIMA(1,1,0)(0,1,1)[12] with drift, with an additionnal MA at lag 3 as when I have fitted an ARIMA(1,1,0)(0,1,1)[12] with drift model, I have seen there was still autocorrelation in ...
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Selecting optimal lag values for Neural Network in univariate time series forecasting - How many lags to use as input variables?
What is the recommended approach for selecting lag values in a univariate time series forecasting problem, specifically for input variables in a feedforward neural network (FFNN)? In my research ...
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How to stationarise a univariate time series in R when auto.arima gives a model to fit that doesn't stationarise it? [closed]
I am currently working on a univariate time series that I try to modelize (following the Box-Jenkins methodology, I try to identify the model before I estimate it, using correlograms, in order to ...
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Expected value of $R^2$ for univariate regression [duplicate]
I'm trying to solve the following: Given a standard one-variable linear regression model $$y = \beta_1 x + \beta_0 + \epsilon$$ where $\beta_1, \beta_0\in\mathbb{R}$ and $\epsilon\sim N(0, \sigma^2)$ ...
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How to apply the Box-Cox transformation to a univariate time series in R?
I have tried to follow the steps indicated on this page and it doesn't work as it doesn't identify the function "recipe" (which I fail to understand anyways...). I am trying to find the best ...
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Negative RSquare Value but Good View on Chart for RNN Time Series
I'm fairly new on univariate time series forecasting and deep learning.
My task is to forecast energy consumption values.
May data looks like:
I'm usin simple RNN, because I have tested with linear ...
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RMSE of Training data is lower compared to test dataset
First of all, this is not a case of Overfitting.
The task is to forecast Temp using univariate Single Step forecasting.
I have trained the LSTM model with on jena climate dataset(Dataset https://...
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Which piece of data should I choose in univariate analysis
What I want is to use univariate analysis to initially identify variables with significant effects for subsequent multivariate analysis.And this is a retrospective study.The data structure is shown in ...
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Relationship between univariate and multivariate normal distribution
I have a portfolio consisting of N assets with known average historical returns ($r_1$, $r_2$, ...$r_N$) and a known set of weights ($a_1$, $a_2$, ...$a_N$) subject to $\sum_{i=1}^N a_i$ = 1
I am ...
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How do I interpret and plot the interaction from a glm in R?
I am currently trying to analyze the duration of the egg stage of two species of insect (factor 1; 2 levels - HA and AP) at several different temperatures (factor 2: 5 levels - 20, 23, 26, 29, 32). ...
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Logistic regression feature ranking reliability using bootstrapping [R]
I wish to display the (un)reliability in ranking of variables in an omics dataset in the classification of disease vs healthy. In this case it’s mostly of academic interest, as many use rank-based ...
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When to specify multivariate versus univariate priors on parameters?
Suppose a linear regression model:
$$y \sim Normal(\beta X, \sigma)$$
For our purposes, assume $y$ is a univariate outcome and $X$ is a design matrix containing an intercept and one additional ...
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How many points should be used on time series linear regression if the time series has rapid changing trends
I am studying a time series that has no seasonal patterns and no overall trend. The trend changes rapidly over time and I want to fit a linear regression model to some windows of the series to make ...
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Why does univariate Mahalanobis distance not match z-score?
I am using Mahalanobis distance for outlier detection. Sometimes my dataset only has 1 feature, sometimes many more. I believe the univariate Mahalanobis distance should be equal to the z-score of the ...
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Non-Continuous Time Series Forecasting
I have the following time series that contains the past 12 months data as well as data for the 24th month:-
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CNN-LSTM or LSTM better for univariate Time series forecasting?
I am working with simulated univariate sequential data and the goal is to forecast that data. I was wondering which model CNN-LSTM or LSTM is better for predicting univariate time series data. Both ...
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How would you show that there is no difference between three groups?
I'm trying to convey data as such:
Unfortunately I have not found a better way than to run a multinomial regression and then show that there at least is no difference between groups A, B and then the ...
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Univariate vs multivariate GLMs for inferring covariate-response relationships with no interactions
I understand that multivariate GLMs/multiple regression are valuable for predicting responses for observations with multiple covariates and for inferring interactive effects of different combinations ...
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Univariate after stepwise regression?
My aim is to understand and describe the effect that independent variables (ordered and unordered factors and continuous) have on a continuous dependent variable.
First, I thought about using a ...
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Joint density and univariate time series models
Is the picture below an example of univariate time series model since I am observing the same random variable just at different time points?
Can joint density be used to explain univariate time series ...
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Under what conditions does univariate variable prescreening fail? (random forest modeling)
Univariate statistical methods are often used to prescreen variables for possible inclusion in a model. For example, running random forests (RF) with only a single predictor at a time to prescreen ...
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Survival analysis - univariable and multivariable regression [duplicate]
I am running an analysis of various factors that determine whether an individual is likely to have an event. My outcome is binary (0 vs 1), and my explicative variables can be both quantitative or ...
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how to quantify the strength of trend in univariate time series data?
I have data that looks something like this:
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How to handle missing data in the univariate analysis
Can someone please advise me on how to handle missing data in a univariate analysis (e.g. t-test, chi-squared test)?
Given that multiple imputation techniques (MICE package) are for multivariate ...
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How does aggregation across time bring about feedback?
On page 11 Pankratz says: "Sometimes there is sound theoretical reasoning in favor of a one-way relationship from the inputs to the output (no feedback). But even then it is important to have a ...
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Regression not significant on univariate but significant when adding controls
I have a data on job type (i.e. dummy variable 1 if bad job and 0 if good job) and amount of debt individuals hold before finding a job. I ran a regression of job type on debt level, and I did not get ...
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Can a (possibly infinite) mixture of Gaussians be Gaussian?
Suppose we define a (possibly infinite) mixture of zero-mean Gaussians:
$$p(x) = \int_{\mathbb{R}^+} N(x; 0, \sigma^2)\ \pi(\sigma)\,\text d\sigma,$$
where $\pi$ defines the mixture components. ...
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How to establish a standardized score for non-normal/long-tail distribution
Apologies if this is an elementary question. My rusty high school understanding of statistics has left me a bit lost to the following problem.
Essentially, I have several long-tail distributions of ...
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Is this an example of a univariate analysis?
Nonstatistician needs help with terms.
Is the following an example of a univariate analysis?: Chances of getting a job at Microsoft with respect to demographic variables such as age, gender, race, etc....
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Rolling vs Recursive vs Fixed Window Regression
What precisely are the differences between rolling, recursive and fixed window regression?
As far as I understand,
recursive: we train on a period $y(0)$ to $y(n)$ then predict $\hat{y}(n+1)$. Then ...
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Covariance of $x_i$ and $\hat{\varepsilon}$ without exogeneity of $\varepsilon$
What is the $Cov(x_i,\hat{\varepsilon})$ if $E(\varepsilon_i|x_i)\neq0$ in a univariate regression model?
Can the expression be simplified further beyond $\sum(\hat{\varepsilon}_i-\bar{\hat{\...
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Find treshold to separate two classes based on single predictor
I have a binary output variable (not healthy, healthy) that I want to classify. I found based on univariate analysis that one of my independent predictors already tells apart both classes perfectly.
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How do I treat zero inflated univariate time series data?
The data I am handling is a rainfall data. The only columns are "Date" and "Rainfall".
The day that is not raining will be accounted to zero, therefore it is a zero inflated ...
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Univariate or Multivariate Time Series?
I believe univariate time series pertain to one single variable changing over time and multivariate refer to multiple variables (either dependant or independent), however the following case is unclear ...
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How to choose variables for multivariable cox regression analysis based on univariable analysis results?
I want to conduct a multivariable cox regression model to assess predictors of "Mortality" variable.
Assume there are three independent variable as follows:
Gender --> (dichotomous= 1,2)
...
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Copula between a distribution and its univariate transformation
I'm trying to compute the copula (or joint distribution) between x and a univariate transformation, like say sin(x).
That is compute $C_{XY}$ (or $F_{XY}$) given that $x \sim U(0,1)$ and $y = sin(x)$
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