Questions tagged [regression]

Techniques for analyzing the relationship between one (or more) "dependent" variables and "independent" variables.

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Continuous mixture distribution

I am currently working on the derivation of the negative binomial distribution as a result of a continuous mixture of Poisson and Gamma distribution for regression purposes. To obtain the entire ...
1 vote
1 answer
27 views

Linear regression on categorical variable, how to interpret the F-statistic?

I am using statsmodels to fit a regression: smf.ols(formula=change ~ C(location))` where change is a continuous variable. I have a lot of locations and some of the ...
3 votes
1 answer
147 views

The best interpretable regression model currently

I'm seeking recommendations for explainable regression models for tabular data. I'm looking for approaches that offer a balance between complexity and interpretability – something more sophisticated ...
1 vote
2 answers
417 views

How to identify coefficients for all levels of categorical variables when you have multiple of them

I have an equation like y ~ x1 + x2 + x3 + x4 where the first 3 variables are categorical and the last one is continues. I want to identify the coefficients for all ...
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What are the conditions to specify the regressors in Heckman 2 step model

I have the issue of interpreting the STATA command Twostep Heckman model, and also adding fixed effects to the model. My analysis is based on a panel dataset and I want to solve for the selection bias ...
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1 answer
524 views

does a scaled multi output neural network result in a scaled RMSE?

Ive scaled my data to be in the range 0-1. ive used this scaled data to train a deep neural network, ...
1 vote
2 answers
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When should I standardize, before or after a regression?

I have a panel dataset and my dependent variable is the logit-transformed share of farm workers on long-term contracts. I am particularly interested in the effects of two variables, pastoral focus in ...
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2 answers
546 views

If I want to adjust survival analysis for a covariate, like age, should I add it "smth+age" or add an interaction with it "smth*age"?

I have a survival analysis with a categorical predictor called "smth". I want to adjust it for age. I don't have any idea if they can interact or not but I guess they can. Now, about the the ...
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1 answer
616 views

Transforming a natural log of a variable into the original variable

I am using a replication dataset for a research, and one variable (GDP per capita) is included in the dataset only as a natural logarithm. Is it possible to transform the ln(GDP per capita) back into ...
2 votes
1 answer
275 views

How to validate a regression model in extrapolation territory?

I'm dealing with a regression problem and have two datasets at my disposal. Dataset A is properly labeled and I use it to fit and validate my model, B is unlabeled and I can only visually inspect ...
2 votes
0 answers
22 views

What model is appropriate for measuring the correlation between two proportions?

Explanation: I have a dataset (say n=100 samples) with two data types associated with each sample (genetic and ecological). I'm resampling the dataset such that I take a random draw of n samples (2, 3,...
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14 views

How to resolve the residual versus predicted quantile devation (Dharma plot)?

I have been trying to perform beta regression modeling with random effects. I have sex ratio as the response variable, and climatic variables + years (1970-2021) as predictor variables. Different ...
1 vote
1 answer
227 views

How to apply EM algorithm in case of mixture distribution?

I am familiar with regression linear models, and EM algorithms. However, I do not get the idea of fitting the mixture of regression linear models using the EM algorithm. So, what I think about it is ...
1 vote
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Potential heteroskedasticity in maximum likelihood

I've created a bad loan classifier model using logit regression and maximum likelihood. The actual v expected comparison of the result is shown below. In order to create the chart, we binned the ...
1 vote
1 answer
317 views

What does it mean when SSR>SST?

Following is an example of the observed and predicted values for my variable y (in R). ...
3 votes
2 answers
175 views

What causes the parameter phi (precision) to be very small in beta regression (by betareg in R)?

I tried to do a beta regression for a variable affected by age and intimacy, but it did not work well. The value of phi (precision) estimated by maximum likelihood method is very small, and when I ...
2 votes
2 answers
517 views

Question about sliding differences contrast coding rationale

I've been reading up on sliding differences coding (forward differences coding, on the ucla page: http://www.ats.ucla.edu/stat/r/library/contrast_coding.htm). Here is the contrast matrix recreated ...
1 vote
1 answer
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Non-Parametric Regression with an Omitted Variable

Suppose we use the Kernel Regression Estimator $$\hat{m}(c)=\frac{\sum_{i=1}^n K\left(\frac{x_i-c}{h}\right)y_i}{\sum_{i=1}^n K\left(\frac{x_i-c}{h}\right)}$$ where $h\to 0$ and $nh\to \infty$ as $n\...
1 vote
1 answer
961 views

How to calculate linearity to the logit when IV includes a meaningful zero (0) and is limited to 6 predefined levels

I am calculating a logistic regression with the amount of letters of a word given (0-5) in a task as the IV and whether the corresponding word was recalled in a later test or not (0/1) as the DV. All ...
2 votes
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35 views

Appropriate stats model for time data with an upper limit?

I'm struggling to settle on a statistical approach for a portion of my dataset. Any thoughts/insight would be appreciated. Subjects (divided into two categorical groups) were given up to an hour to ...
0 votes
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8 views

determining if odds-ratio for a numerical x variable and binary y label in logistic regression differs between groups (ie. gender or race) [duplicate]

I've seen people ask a similar question, but I'm still not super clear what the best recommendation would be - I am looking at a binary dependent variable, and multiple independent variables (some of ...
4 votes
0 answers
47 views
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Truncated response variable in boosted regression trees

I was thinking about the differences in approaches between parametric and non-parametric statistics in regression. I am working with a non-negative integer response $N\in\mathbb{N}_{0}$. Let's imagine ...
12 votes
5 answers
7k views

Computing gradients via Gaussian Process Regression

I have a set of noisy data that I am fitting using Gaussian Process Regression via Python's sklearn package. The posterior mean of the GP is essentially my output with an associated error. Based on ...
154 votes
8 answers
107k views

Why L1 norm for sparse models

I am reading books about linear regression. There are some sentences about the L1 and L2 norm. I know the formulas, but I don't understand why the L1 norm enforces sparsity in models. Can someone give ...
1 vote
1 answer
924 views

Weibull Regression of Left Truncated Data in R

I have a data set of tree diameters that do not include any measurements below a 7.5 due to the difficulty in identifying species when they are small. I want to run a Weibull regression that returns ...
0 votes
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confidence interval over point estimate given regression parameters?

In Bayesian analysis, the poster distribution is often sampled when the PDF is not intractable (often). If the samples are of length $n$ then every index in $range(1,n)$ corresponds to a valid sample ...
2 votes
2 answers
2k views

Raw eigenvectors or standardized eigenvectors for principal component regression?

Thanks to @amoeba, I learned that standardized eigenvectors are sometimes calculated, i.e., eigenvectors are divided by the square root of their eigenvalues. Now, when I want to do principal ...
6 votes
1 answer
244 views

Match model selection strategies with modelling objectives

I am confused trying to match different model selection strategies with different modelling objectives. (Unfortunately, my confusion is reflected in the length of the post. Please be patient.) Model ...
2 votes
1 answer
39 views

How to simulate non-standardized artificial data for logistic regression?

I would like to simulate data for a logistic regression with the predictor variables on their original scale. There are, of course, a litany of similar previous questions (e.g., here, here, and here). ...
1 vote
1 answer
473 views

Test for comparing x-intercepts of two linear regression

I would like to know how to compare, and then calculate significant differences, if any, between the x-intercepts of two regression lines. Practically, I should compare the values of x when y=0. I ...
0 votes
1 answer
36 views

question about including an independent variable

I'm seeking advice on whether to include the independent variable 'smoking' (Yes/No) in my analysis. The objective is to examine the impact of COVID-19 on female construction workers. The outcome ...
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9 views

Struggling with normalization/Standardisation for machine learning dataset [duplicate]

Sorry for what is probably a very obvious/rookie question. I'm currently doing a data science module for my degree and making very slow progress with the work. The case study i'm doing is around HR ...
0 votes
1 answer
23 views

Different estimates of conditional mean parameters from OLS vs ARCH

Consider the market model for security $i$: $$ R_{i,t}=\alpha_i + \beta_i R_{m,t} + e_{i,t}. $$ I estimated the parameters with the OLS method. ...
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Variability in the dataset explained by a subset of variables

Let's assume a centred data matrix $X_{n \times p}$ of $p$ variables, where each variable has unit variance. PCA can be thought about as a rotation of the coordinate system that diagonalises the ...
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1 answer
55 views

Can Phi coefficient for intercept be negative?

I am working on beta regression model with two grouping variables (farm and years). Climatic variables are my predictors. Response variable is male proportion. I standardized all variables prior to ...
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Interpretation of negative coefficients in ordinal regression in spss

I have built an ordinal regression (logit) in the spss for data in the likert scales. I put the independent variables into factors, not covariates. As far as I understand, this should be done with ...
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21 views

What is the ARMAX model specification of the following economic setting?

I am currently doing a project estimating electricity prices in France, however, my modelling skills are lacking. I have hourly data on spot prices, which are determined per separate hour, one day ...
1 vote
1 answer
759 views

Power Analysis for Difference in Differences Regression

How would you approach conducting a power analysis to estimate how large of a sample you would need to conduct a difference in differences estimate? For example, for a T-test, I would just set effect ...
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29 views

Can you store the value of the predicted variable (Y) at each fold and then correlate the predicted values with the actual data?

In particular, imagine to have a set of features (X) that I use to predict a continuos variable (Y). Is it possible to use elastic net, in a cross-validation framework, use it to predict the value of ...
1 vote
1 answer
41 views

Can I utilize Ridge Regression to update coefficients of a Linear Regression model for a new dataset?

I have fitted a Linear Regression Model using one dataset. Now, I have another smaller dataset that I want to refine the model with. Can I use Ridge regression to update the estimated coefficients for ...
2 votes
1 answer
555 views

Estimated marginal means against raw mean when only one predictor

I'm learning about estimated marginal means and I found this very interesting tutorial about it. I get almost all of it, especially the fact that with a multivariate analysis we can extract modelled ...
1 vote
1 answer
356 views

Odds ratio interpretation for generalized additive models

This is probably a very crude question and I've been thinking about it for a while. Is the odds ratio the same for generalized additive models (GAMs) as it is with generalized linear models (GLMs)? If ...
2 votes
0 answers
26 views

Correct regression to use for longitudinal pest survey between two groups across multiple sites

THE DATA: We have 3 sites with a total of 36 plants growing in them: each site contains 12 plants (6 belonging to spp1 and 6 belonging to spp2). The size and geolocation of each plant is known. Each ...
3 votes
1 answer
591 views

Bayesian Regression- Expectation Maximization

In Bayesian regression, we have $y_i=x_i^{T}w+\epsilon_i$ where $w \sim \mathcal{N}(0,\alpha)$ and $\epsilon_i \sim \mathcal{N}(0,\frac{1}{\beta})$. Inference of $\alpha$ and $\beta$ is done by ...
1 vote
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27 views

Does a significant interaction & effect imply an effect in both groups? [duplicate]

I'm fitting an interaction model for two treatment groups: water and saline. Participants of the study, at different ages $X$, begin the treatment and $Y$ is measured. I fit an interaction model $$Y = ...
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Confidence intervals around treatment effect in stratified experiment

Suppose you conduct an experiment, where a population is randomized and treatment is given to the treatment group and you are interested in covariates (stratification has been employed) and their ...
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18 views

Moving Average of Linear Regressions

I have a dataset of n points $\mathcal{D}_n=\left\{(x_i,y_i)\right\}_1^n$ with the dataset is ordered by the values of x. We can assume that $y_i=f(x_i)+\epsilon_i$, mean zero errors and the response ...
30 votes
4 answers
42k views

What is the problem with using R-squared in time series models?

I have read that using R-squared for time series is not appropriate because in a time series context (I know that there are other contexts) R-squared is no longer unique. Why is this? I tried to look ...
0 votes
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189 views

Force a rma.mv fitted model to have the intercept value equal to 1.0 in R

I'm new in this blog and my knowledge in R is very weak. However, I was trying to set the intercept at 1 in the following rma.mv ('metafor' package) function: ...
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Creating a light image generation model for a specific distribution

I am currently working on how a user can introduce bias in a neural network model. To do so, I am creating an image2image model that only works on the training distribution. For example, let's say I ...

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