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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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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 ...
mkadz's user avatar
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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 ...
Mario's user avatar
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2 votes
1 answer
82 views

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 ...
Madamadam's user avatar
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11 views

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 ...
Epic Cabbage's user avatar
2 votes
1 answer
96 views

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 ...
Mario's user avatar
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0 answers
37 views

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, ...
weezy79's user avatar
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1 answer
50 views

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 : ...
EconQC's user avatar
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0 answers
110 views

Correcting p-values when accounting for age and sex in a study where the main objective is to find differences between disease/control

So I analyzed my metabolomics data using Mann Whitney U tests and OPLS-DA. My data is super small, with the control group only having 5 samples and the other groups having 10 and 14 samples ...
maglorismyspiritanimal's user avatar
0 votes
1 answer
85 views

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 ...
Raj's user avatar
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3 votes
2 answers
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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 ...
noNameTed's user avatar
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1 answer
56 views

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 ...
Mr. Panda's user avatar
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1 vote
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60 views

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: ...
Restu's user avatar
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0 answers
29 views

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 ...
gerardlambert's user avatar
2 votes
0 answers
109 views

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 ...
rashmi's user avatar
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0 answers
16 views

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 ...
gerardlambert's user avatar
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41 views

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 $\epsilon\sim N(0, \sigma^2)$ ...
vyjtkbyykyhuk's user avatar
1 vote
1 answer
242 views

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 ...
gerardlambert's user avatar
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0 answers
25 views

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 ...
Beyza's user avatar
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1 vote
0 answers
57 views

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://...
Hemant Yadav's user avatar
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126 views

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 ...
Kevin Song's user avatar
2 votes
0 answers
90 views

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 ...
Nikowhy's user avatar
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1 vote
0 answers
76 views

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). ...
Insect_biologist's user avatar
2 votes
1 answer
149 views

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 ...
rstats_enthusiast's user avatar
2 votes
0 answers
131 views

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 ...
socialscientist's user avatar
0 votes
1 answer
27 views

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 ...
marialonsogar's user avatar
7 votes
1 answer
1k views

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 ...
kwinkunks's user avatar
  • 349
1 vote
2 answers
1k views

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:- ...
user361234's user avatar
1 vote
0 answers
1k views

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 ...
Quinten's user avatar
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1 vote
0 answers
51 views

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 ...
Paze's user avatar
  • 2,351
0 votes
1 answer
271 views

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 ...
smbritton's user avatar
0 votes
0 answers
114 views

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 ...
csdori's user avatar
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0 votes
0 answers
158 views

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 ...
Shetu's user avatar
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1 vote
0 answers
105 views

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 ...
Kyle's user avatar
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0 votes
0 answers
34 views

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 ...
Svalf's user avatar
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0 votes
0 answers
245 views

how to quantify the strength of trend in univariate time series data?

I have data that looks something like this: ...
Naveen Reddy Marthala's user avatar
1 vote
2 answers
386 views

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 ...
R Beginner's user avatar
1 vote
0 answers
29 views

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 ...
ColorStatistics's user avatar
5 votes
2 answers
3k views

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 ...
David Kim's user avatar
9 votes
1 answer
526 views

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. ...
Allen94's user avatar
  • 91
0 votes
1 answer
427 views

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 ...
ConsistentC's user avatar
1 vote
1 answer
323 views

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....
Eggy's user avatar
  • 121
2 votes
1 answer
4k views

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 ...
charelf's user avatar
  • 243
0 votes
1 answer
50 views

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{\...
Steven's user avatar
  • 3
1 vote
1 answer
32 views

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. ...
Sapiens's user avatar
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1 vote
0 answers
746 views

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 ...
Seow's user avatar
  • 13
3 votes
2 answers
3k views

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 ...
stackoverflowname's user avatar
2 votes
1 answer
2k views

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) ...
Behnam Hedayat's user avatar
0 votes
1 answer
159 views

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 $y = sin(x)$ ...
A. Gray's user avatar
  • 41
0 votes
3 answers
210 views

Selection of variables based on clinical knowledge in multiple logistic regression

Dear Cross Validated community, I am conducting a multiple logistic regression for a case-control study. There are too many predictors to be investigated. My question is "Can I choose which ...
Ala' Shaban's user avatar