Questions tagged [standardization]

Usually refers to "z-standardization" which is shifting and rescaling data to assure they have zero mean and unit variance. Other "standardizations" are possible, too.

Filter by
Sorted by
Tagged with
0 votes
0 answers
5 views

Does the "type" of standard error affect the relationship between the standard errors of standardized vs. unstandardized estimates?

While previous answers explain how to convert between standardized and unstandardized coefficients and their standard errors, it is not clear if this formula applies across different types of standard ...
user avatar
0 votes
0 answers
7 views

Why is it desirable to standardize the inputs of a Gaussian Process Regression?

I read this question (Should we standardize the data while doing Gaussian process regression?) and wondered, why do we need to normalize the inputs of a Gaussian Process? In my case, I want to use ...
user avatar
  • 1
0 votes
0 answers
16 views

Dependent variable standardization in neural networks

I am using a multilayer perceptron model to predict urban temperatures. I have standardized the independent variables before training the model. However, I have not standardized the dependent variable....
user avatar
0 votes
0 answers
13 views

Feature preprocessing (standardize and normalize) and variable independence

I can't find clarity on this question so here goes: Suppose I have 3 features, x, y, z. I know x and ...
user avatar
  • 143
0 votes
1 answer
23 views

When/what to standardize for model-averaging with(out) interactions

I’m using the {MuMIn} package in R to select models (dredge, get top models, average etc). My question is about whether I need to, or should, standardise my ...
user avatar
  • 1
0 votes
0 answers
24 views

Standardization in Machine Learning Models

Please answer this question in two contexts: Context 1 - Performance: Which models are sensitive AND which models are insensitive to standardization? Why? Are there any edge-cases in which ...
user avatar
2 votes
1 answer
26 views

Using random intercepts or z-standardizing within factors: Two identical ways to account for variance between factors?

I have the following (simple?) question about statistics: I have a dataset where I look for correlations between variables and would like to control for differences between factor levels. For ...
user avatar
  • 21
0 votes
0 answers
18 views

Computing distance and standarization of features

Intro: suppose we have $n$ observations with $m$ features, represented by a $n\times m$-matrix $X$, and two specific points $x,y\in\mathbb{R}^m$, and we are interested in distances between $X$ and $x,...
user avatar
0 votes
0 answers
44 views

p-values of standardized vs non-standardized regression model coefficients - are they the same?

I made the following simple regression model and used stargazer to output a table that plots the standardized vs non-standardized regression model coefficients. ...
user avatar
2 votes
1 answer
24 views

Derivatives of output w.r.t input on a neural network trained with standardized data

I'm using a neural network to model an unknown function for which I would also like to know the derivatives. The nn has four inputs and four outputs, and the training data is preprocessed using scikit-...
user avatar
  • 25
1 vote
1 answer
49 views

Do I need to transform/standardise my dependent variable?

Attached are the results and the residual plot for my regression of control variables on CEO compensation (TDC1). When I look at the plot my main concerns are the outliers (which I checked to be ...
user avatar
0 votes
0 answers
12 views

Normalization on 2 dimensions

I have alot of stock data features. I have normalized each feature by stock as different stocks have very different stdev. I would also like to normalize by time of day. IE stdev is very different ...
user avatar
0 votes
0 answers
47 views

How do I make different distributions objectively comparable?

I have some model, $f(x)$, that takes in some input and produces some output. Now my input are random variables distributed according to some distribution. The output are also random variables with ...
user avatar
  • 203
1 vote
0 answers
11 views

What happens to a VAR when I standarize variables?

Suppose you have 6 or 7-time series that are stationary. But my question is what does happen to a VAR when I standardize all the variables of the VAR This makes that the mean is 0 and the standard ...
user avatar
0 votes
0 answers
15 views

Appropriate standardized solution for a CFA of one latent variable, and multiple ordinal indicators

I am currently working on a CFA in OpenMx, where a standardized latent variable is estimated by multiple ordinal indicators with means and variances restricted to 0 and 1, respectively. According to ...
user avatar
1 vote
1 answer
71 views

What is the difference between scaling to "unit variance" and "standardization"?

So what i understand is that scaling by unit variance is dividing the values by the standard deviation. While standardization is subtracting the mean and then dividing by the standard deviation. In ...
user avatar
  • 11
1 vote
1 answer
76 views

Does brms automatically standardise data and/or coefficients?

I'm running some regression models in R using brms and lme4. When I run a Bayesian model: ...
user avatar
0 votes
0 answers
10 views

Compare hierarchical clustering results with different imporance scaling

For an assignment at university, I am using hierarchical clustering of a dataset of 5 different parameters. The parameters all have different magnitudes, so I standardized the data using a z-score ...
user avatar
0 votes
1 answer
25 views

Standardisation with respect to controls

When performing analyses using polygenic risk scores (PRS), Why is it important to standardise/normalise PRSes using mean and sd derived from control samples, before performing analyses, such as ...
user avatar
  • 103
1 vote
0 answers
18 views

Normalization of time series data for input into Neural Network, values will vary over time

I was reading up on how to normalize stock time series data for input into a neural network. What I've seen suggests things like min-max normalization and z-score normalization. The issue I see is, ...
user avatar
1 vote
2 answers
29 views

Standardization and test prediction in linear regression

I am considering a linear regression model to predict a target $y\in\mathbb{R}^n$ from a data matrix $X\in\mathbb{R}^{n\times d}$. Let $X_1,\ldots,X_d\in\mathbb{R}^n$ be the columns of $X$. The ...
user avatar
0 votes
1 answer
24 views

Interpreting results of a regression model with interactions between three independent discrete variables

I am running a multiple linear regression with interactions between the three independent variables (P, R and L). Y is a continuous variable and P, R and L are discrete variables (in particular they ...
user avatar
  • 113
0 votes
1 answer
25 views

Age adjustment or Age standardization state of the art?

I want to run some comparisons of a quantity between different populations. Such quantity comes from a model that includes, among other variables, the variable age. So, one of the variables affecting ...
user avatar
1 vote
0 answers
49 views

Can I standardize data twice? Creating a composite covariate in regression

I am running a regression analyses with several covariates, and my goal is to examine the relative influence of each covariate on the dependent variable. Therefore, I have taken the approach of ...
user avatar
0 votes
1 answer
20 views

Does values standardization affect its distrution?

I have Wald's test results from comparing sensitivity and specificity of LDA and QDA. Results are almost the same. I generate data, train and classify it and then obtain the Wald statistic. I need to ...
user avatar
0 votes
0 answers
29 views

Do I need to standardized age and the outcomes if I want to do a multivariable regression

I want to conduct a multivariate regression. I decided to standardize other variables such as exposure score. Do I need to standardize age? It sounds weird if I say increasing 1 SD age is associated ...
user avatar
0 votes
0 answers
17 views

Standardise by sub-group mean or overall mean?

I am looking at the difference between genders on a mobility metric and I would like to evaluate the temporal patterns of this metric, I have already assessed the differences in the distributions, and ...
user avatar
  • 1
0 votes
1 answer
38 views

In regression when we standardise the data do we need intercept?

I would like to see when we standradize the data and then apply linear regression or Bayesian regression do we need intercept or no? or it has nothing with standardize?
user avatar
  • 123
0 votes
2 answers
60 views

Standardized coefficients in multilevel-models to compare predictor influence

Suppose I conducted an experience sampling study and set up a multi-level regression, in which episodic well-being (LVL-1) is predicted by episodic flow (LVL-1) and episodic loneliness (LVL-1) (...
user avatar
1 vote
1 answer
34 views

Does it make sense to standardize Principal Components after performing Principal Component Analysis?

I am attempting to emulate the following paper by Messer et al., using year 2000 decennial Census data in R to create an index known as the Neighborhood Deprivation Index(NDI): https://www.ncbi.nlm....
user avatar
  • 13
2 votes
0 answers
33 views

Variance sensitivity of dataset with scaling

What I have is 3 datasets scaled with RobustScaler, MinMaxScaler and StandardScaler. However ...
user avatar
2 votes
1 answer
310 views

Why not both standardize and normalize features for machine learning?

If one has data that's assumed to be normal distributed and want to use it as input in a machine learning model, why not first standardize the data and then normalize (min max scale it between zero ...
user avatar
0 votes
0 answers
77 views

Normalization and RidgeCV in Sklearn Pipeline - possible data leakage?

To avoid data leakage between the train and test set, I'm using sklearn's Pipeline as follows: ...
user avatar
0 votes
0 answers
24 views

Pitfalls using ratio of mean change to standard deviation of change - i.e. longitudinal MSDR

A number of biostatistics papers use the mean-to-standard-deviation ratio (MSDR) of change to summarise symptomatic progression on various clinical assessment scales. This is done to compare disease ...
user avatar
0 votes
0 answers
33 views

Why doesn't the t distribution depend on variance of the original normal distribution?

I just find it counter intuitive that, for an arbitrary normal distribution with 0 mean and unknown variance, the t statistic of $\frac{\bar{X}}{\hat{se}} $ is completely independent of the variance ...
user avatar
  • 103
0 votes
0 answers
23 views

Standardisation and Logistic Regression

I read that "Standardization isn’t required for logistic regression. The main goal of standardizing features is to help convergence of the technique used for optimization." Although Logistic ...
user avatar
1 vote
1 answer
26 views

How to use batch norm to perform input standardization?

I need to train a model with an un-normalized dataset and I can not directly standardize it (subtract the mean and divide by the std), but I do have the mean and std for each feature. Thus I'm ...
user avatar
0 votes
2 answers
74 views

Why Should We Standardize Regression Variables?

Many textbooks and articles (such as this one) advise to standardize variables before entering them into our regression models; i.e., (variable - mean) / standard deviation However, I just came across ...
user avatar
0 votes
0 answers
95 views

Back transforming from a log-transformed and subsequently standardized (outcome) variable

Due to skewness of my data I've performed a natural log transformation of my outcome variables, dealing with scores of 0 by adding 1 to all measurements. Then, to be able to compare relationships ...
user avatar
  • 119
0 votes
1 answer
69 views

Alternative data standardization procedures before PCA analysis

I'm working with morphometric data including 50 different variables (with very different scales) measured in 180 individuals (these 180 individuals belong to 4 different groups which received ...
user avatar
  • 1
0 votes
0 answers
78 views

Determine when a value has stabilized

Q: Given a set of data points, what is the best way to determine when the value has stabilized? I have sales data for multiple products since their launch and want to find the place where their demand ...
user avatar
0 votes
0 answers
18 views

z-scores of a series of percentages [duplicate]

Suppose I have a series of absolute values, for example the number of tourists in four different regions in a given year, let's say [100000, 200000, 50000, 1000000]. If I wanted to calculate the z-...
user avatar
  • 121
1 vote
0 answers
35 views

Should I use StandardScaler on non-stationary data?

When I'm using StandardScaler on non-stationary data, like exponential global temperature, I use it as follows: ...
user avatar
0 votes
0 answers
35 views

How does the GLMNET standardize = TRUE option work?

I am struggling to find information about how the standardize = TRUE option as part of the glmnet or cv.glmnet calls works. My understanding (although it might be wrong) is that when the ...
user avatar
  • 101
1 vote
1 answer
69 views

Transform variables with zero-inflated values and positive skewness

I have over 30 features: several have zero-inflated and highly positive skewed distribution. Those distributions are expected because they are semi-continuous monetary related features. For example: ...
user avatar
0 votes
1 answer
59 views

Interpret regression coefficients when dependent variable is standardized

Let's say we have the following regression model: $$z_i = \beta_0 + \beta_1 X_{1,i} + \beta_2 X_{2,1} + u_i$$ Where $z_i = \frac{y_i - \bar{y}}{\sigma_y}$ is the (standardized) dependent variable. How ...
user avatar
0 votes
0 answers
34 views

Why is in order to normalise t-distribution the following function is used $t = z*s$, shouldn't it be $t=z*\frac{\sigma}{s}$?

I am trying to answer the following question: Suppose there is a new genetically modified species of pine, with an expected fully grown height of 14 metres and a standard deviation of one metre. The ...
user avatar
1 vote
0 answers
2k views

z-score VS min-max normalization

Working with data that use different dimensions, you do not want that one dimension dominate. This means feature scaling! A very intuitive way is to use min-max scaling so you scale everything between ...
user avatar
0 votes
0 answers
69 views

calculate vif before or after standardization

hi I calculate vif before standardization results are: feature VIF 0 7.372450 1 2.116071 2 1.283643 3 1.268624 4 1.730986 5 5.442314 6 1.266718 7 3.183669 after ...
user avatar
0 votes
1 answer
24 views

Rescaling random intercept coefficients from hierarchical logistic regression

My logistic regression model includes an overall intercept, multiple categorical variables + and continuous covariates like so: $logit(\mu)$ = $\beta_0$ + $\alpha_{j}$ + $\gamma_k$ + $\beta$$X$ where $...
user avatar
  • 1

1
2 3 4 5
15